Overview

Dataset statistics

Number of variables111
Number of observations1190
Missing cells9976
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory923.8 B

Variable types

Numeric14
Categorical97

Alerts

KFK_BLOOD has constant value "1.8"Constant
ALT_BLOOD is highly overall correlated with AST_BLOOD and 2 other fieldsHigh correlation
ANT_CA_S_n is highly overall correlated with fibr_ter_08High correlation
ASP_S_n is highly overall correlated with fibr_ter_08High correlation
AST_BLOOD is highly overall correlated with ALT_BLOOD and 2 other fieldsHigh correlation
B_BLOK_S_n is highly overall correlated with fibr_ter_08High correlation
DLIT_AG is highly overall correlated with GBHigh correlation
D_AD_KBRIG is highly overall correlated with K_SH_POST and 12 other fieldsHigh correlation
D_AD_ORIT is highly overall correlated with S_AD_ORIT and 6 other fieldsHigh correlation
FK_STENOK is highly overall correlated with IBS_POST and 3 other fieldsHigh correlation
GB is highly overall correlated with DLIT_AGHigh correlation
GEPAR_S_n is highly overall correlated with fibr_ter_08High correlation
GIPER_NA is highly overall correlated with n_p_ecg_p_05 and 2 other fieldsHigh correlation
GIPO_K is highly overall correlated with K_BLOOD and 3 other fieldsHigh correlation
GT_POST is highly overall correlated with IBS_NASLHigh correlation
IBS_NASL is highly overall correlated with GT_POST and 33 other fieldsHigh correlation
IBS_POST is highly overall correlated with FK_STENOK and 1 other fieldsHigh correlation
IM_PG_P is highly overall correlated with IBS_NASLHigh correlation
K_BLOOD is highly overall correlated with GIPO_K and 3 other fieldsHigh correlation
K_SH_POST is highly overall correlated with D_AD_KBRIG and 2 other fieldsHigh correlation
LID_KB is highly overall correlated with fibr_ter_05 and 3 other fieldsHigh correlation
LID_S_n is highly overall correlated with fibr_ter_08High correlation
L_BLOOD is highly overall correlated with ritm_ecg_p_06High correlation
MP_TP_POST is highly overall correlated with ritm_ecg_p_02High correlation
NA_BLOOD is highly overall correlated with n_p_ecg_p_05 and 2 other fieldsHigh correlation
NA_KB is highly overall correlated with fibr_ter_05 and 4 other fieldsHigh correlation
NITR_S is highly overall correlated with fibr_ter_08High correlation
NOT_NA_KB is highly overall correlated with fibr_ter_05 and 4 other fieldsHigh correlation
STENOK_AN is highly overall correlated with FK_STENOK and 2 other fieldsHigh correlation
SVT_POST is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
S_AD_KBRIG is highly overall correlated with D_AD_KBRIG and 12 other fieldsHigh correlation
S_AD_ORIT is highly overall correlated with D_AD_ORIT and 7 other fieldsHigh correlation
TIKL_S_n is highly overall correlated with fibr_ter_08High correlation
TRENT_S_n is highly overall correlated with fibr_ter_08High correlation
ant_im is highly overall correlated with nr_07High correlation
endocr_03 is highly overall correlated with IBS_NASLHigh correlation
fibr_ter_01 is highly overall correlated with IBS_NASLHigh correlation
fibr_ter_02 is highly overall correlated with IBS_NASLHigh correlation
fibr_ter_05 is highly overall correlated with D_AD_KBRIG and 5 other fieldsHigh correlation
fibr_ter_06 is highly overall correlated with D_AD_KBRIG and 3 other fieldsHigh correlation
fibr_ter_08 is highly overall correlated with ALT_BLOOD and 17 other fieldsHigh correlation
inf_im is highly overall correlated with nr_07High correlation
lat_im is highly overall correlated with nr_07High correlation
n_p_ecg_p_01 is highly overall correlated with IBS_NASLHigh correlation
n_p_ecg_p_04 is highly overall correlated with IBS_NASLHigh correlation
n_p_ecg_p_05 is highly overall correlated with ALT_BLOOD and 16 other fieldsHigh correlation
n_p_ecg_p_06 is highly overall correlated with IBS_NASLHigh correlation
n_p_ecg_p_08 is highly overall correlated with IBS_NASLHigh correlation
n_p_ecg_p_09 is highly overall correlated with IBS_NASLHigh correlation
n_r_ecg_p_02 is highly overall correlated with IBS_NASLHigh correlation
n_r_ecg_p_05 is highly overall correlated with ritm_ecg_p_02High correlation
n_r_ecg_p_06 is highly overall correlated with IBS_NASL and 2 other fieldsHigh correlation
n_r_ecg_p_08 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
n_r_ecg_p_09 is highly overall correlated with D_AD_KBRIG and 5 other fieldsHigh correlation
n_r_ecg_p_10 is highly overall correlated with D_AD_KBRIG and 2 other fieldsHigh correlation
np_01 is highly overall correlated with D_AD_ORIT and 2 other fieldsHigh correlation
np_04 is highly overall correlated with D_AD_KBRIG and 2 other fieldsHigh correlation
np_05 is highly overall correlated with IBS_NASLHigh correlation
np_07 is highly overall correlated with D_AD_KBRIG and 8 other fieldsHigh correlation
np_08 is highly overall correlated with IBS_NASLHigh correlation
np_09 is highly overall correlated with D_AD_KBRIG and 4 other fieldsHigh correlation
np_10 is highly overall correlated with D_AD_KBRIG and 8 other fieldsHigh correlation
nr_01 is highly overall correlated with IBS_NASLHigh correlation
nr_04 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
nr_07 is highly overall correlated with IBS_NASL and 4 other fieldsHigh correlation
post_im is highly overall correlated with nr_07High correlation
ritm_ecg_p_01 is highly overall correlated with np_07 and 1 other fieldsHigh correlation
ritm_ecg_p_02 is highly overall correlated with MP_TP_POST and 3 other fieldsHigh correlation
ritm_ecg_p_04 is highly overall correlated with IBS_NASL and 1 other fieldsHigh correlation
ritm_ecg_p_06 is highly overall correlated with D_AD_KBRIG and 13 other fieldsHigh correlation
ritm_ecg_p_07 is highly overall correlated with np_07 and 1 other fieldsHigh correlation
ritm_ecg_p_08 is highly overall correlated with np_07High correlation
zab_leg_03 is highly overall correlated with IBS_NASLHigh correlation
zab_leg_06 is highly overall correlated with IBS_NASLHigh correlation
SIM_GIPERT is highly imbalanced (79.2%)Imbalance
ZSN_A is highly imbalanced (73.6%)Imbalance
nr_11 is highly imbalanced (81.3%)Imbalance
nr_01 is highly imbalanced (98.2%)Imbalance
nr_02 is highly imbalanced (90.2%)Imbalance
nr_03 is highly imbalanced (87.2%)Imbalance
nr_04 is highly imbalanced (90.8%)Imbalance
nr_07 is highly imbalanced (99.0%)Imbalance
nr_08 is highly imbalanced (98.2%)Imbalance
np_01 is highly imbalanced (99.0%)Imbalance
np_04 is highly imbalanced (98.2%)Imbalance
np_05 is highly imbalanced (93.0%)Imbalance
np_07 is highly imbalanced (99.0%)Imbalance
np_08 is highly imbalanced (97.5%)Imbalance
np_09 is highly imbalanced (98.2%)Imbalance
np_10 is highly imbalanced (99.0%)Imbalance
endocr_02 is highly imbalanced (86.7%)Imbalance
endocr_03 is highly imbalanced (93.0%)Imbalance
zab_leg_01 is highly imbalanced (52.3%)Imbalance
zab_leg_02 is highly imbalanced (71.9%)Imbalance
zab_leg_03 is highly imbalanced (90.8%)Imbalance
zab_leg_04 is highly imbalanced (97.5%)Imbalance
zab_leg_06 is highly imbalanced (88.2%)Imbalance
O_L_POST is highly imbalanced (73.9%)Imbalance
K_SH_POST is highly imbalanced (98.2%)Imbalance
MP_TP_POST is highly imbalanced (73.5%)Imbalance
SVT_POST is highly imbalanced (95.4%)Imbalance
GT_POST is highly imbalanced (96.0%)Imbalance
FIB_G_POST is highly imbalanced (94.8%)Imbalance
post_im is highly imbalanced (61.3%)Imbalance
IM_PG_P is highly imbalanced (85.8%)Imbalance
ritm_ecg_p_02 is highly imbalanced (75.4%)Imbalance
ritm_ecg_p_04 is highly imbalanced (94.3%)Imbalance
ritm_ecg_p_06 is highly imbalanced (98.9%)Imbalance
ritm_ecg_p_08 is highly imbalanced (79.8%)Imbalance
n_r_ecg_p_01 is highly imbalanced (77.3%)Imbalance
n_r_ecg_p_02 is highly imbalanced (95.9%)Imbalance
n_r_ecg_p_04 is highly imbalanced (74.0%)Imbalance
n_r_ecg_p_05 is highly imbalanced (82.1%)Imbalance
n_r_ecg_p_06 is highly imbalanced (89.2%)Imbalance
n_r_ecg_p_08 is highly imbalanced (97.3%)Imbalance
n_r_ecg_p_09 is highly imbalanced (99.0%)Imbalance
n_r_ecg_p_10 is highly imbalanced (98.1%)Imbalance
n_p_ecg_p_01 is highly imbalanced (98.1%)Imbalance
n_p_ecg_p_03 is highly imbalanced (89.1%)Imbalance
n_p_ecg_p_04 is highly imbalanced (98.1%)Imbalance
n_p_ecg_p_05 is highly imbalanced (99.0%)Imbalance
n_p_ecg_p_06 is highly imbalanced (93.8%)Imbalance
n_p_ecg_p_07 is highly imbalanced (66.8%)Imbalance
n_p_ecg_p_08 is highly imbalanced (96.6%)Imbalance
n_p_ecg_p_09 is highly imbalanced (93.8%)Imbalance
n_p_ecg_p_10 is highly imbalanced (88.1%)Imbalance
n_p_ecg_p_11 is highly imbalanced (86.0%)Imbalance
n_p_ecg_p_12 is highly imbalanced (78.1%)Imbalance
fibr_ter_01 is highly imbalanced (93.0%)Imbalance
fibr_ter_02 is highly imbalanced (95.4%)Imbalance
fibr_ter_03 is highly imbalanced (77.9%)Imbalance
fibr_ter_05 is highly imbalanced (97.4%)Imbalance
fibr_ter_06 is highly imbalanced (95.4%)Imbalance
fibr_ter_07 is highly imbalanced (96.1%)Imbalance
fibr_ter_08 is highly imbalanced (99.0%)Imbalance
GIPER_NA is highly imbalanced (83.1%)Imbalance
R_AB_1_n is highly imbalanced (53.7%)Imbalance
R_AB_2_n is highly imbalanced (67.4%)Imbalance
R_AB_3_n is highly imbalanced (81.6%)Imbalance
NITR_S is highly imbalanced (64.9%)Imbalance
NA_R_2_n is highly imbalanced (76.4%)Imbalance
NA_R_3_n is highly imbalanced (86.0%)Imbalance
NOT_NA_1_n is highly imbalanced (53.3%)Imbalance
NOT_NA_2_n is highly imbalanced (76.3%)Imbalance
NOT_NA_3_n is highly imbalanced (79.1%)Imbalance
TIKL_S_n is highly imbalanced (85.2%)Imbalance
STENOK_AN has 45 (3.8%) missing valuesMissing
FK_STENOK has 17 (1.4%) missing valuesMissing
IBS_NASL has 1124 (94.5%) missing valuesMissing
DLIT_AG has 148 (12.4%) missing valuesMissing
S_AD_KBRIG has 760 (63.9%) missing valuesMissing
D_AD_KBRIG has 760 (63.9%) missing valuesMissing
S_AD_ORIT has 249 (20.9%) missing valuesMissing
D_AD_ORIT has 249 (20.9%) missing valuesMissing
K_SH_POST has 12 (1.0%) missing valuesMissing
ant_im has 29 (2.4%) missing valuesMissing
lat_im has 24 (2.0%) missing valuesMissing
inf_im has 23 (1.9%) missing valuesMissing
post_im has 22 (1.8%) missing valuesMissing
ritm_ecg_p_01 has 112 (9.4%) missing valuesMissing
ritm_ecg_p_02 has 112 (9.4%) missing valuesMissing
ritm_ecg_p_04 has 112 (9.4%) missing valuesMissing
ritm_ecg_p_06 has 112 (9.4%) missing valuesMissing
ritm_ecg_p_07 has 112 (9.4%) missing valuesMissing
ritm_ecg_p_08 has 112 (9.4%) missing valuesMissing
n_r_ecg_p_01 has 76 (6.4%) missing valuesMissing
n_r_ecg_p_02 has 76 (6.4%) missing valuesMissing
n_r_ecg_p_03 has 76 (6.4%) missing valuesMissing
n_r_ecg_p_04 has 76 (6.4%) missing valuesMissing
n_r_ecg_p_05 has 76 (6.4%) missing valuesMissing
n_r_ecg_p_06 has 76 (6.4%) missing valuesMissing
n_r_ecg_p_08 has 76 (6.4%) missing valuesMissing
n_r_ecg_p_09 has 76 (6.4%) missing valuesMissing
n_r_ecg_p_10 has 76 (6.4%) missing valuesMissing
n_p_ecg_p_01 has 77 (6.5%) missing valuesMissing
n_p_ecg_p_03 has 77 (6.5%) missing valuesMissing
n_p_ecg_p_04 has 77 (6.5%) missing valuesMissing
n_p_ecg_p_05 has 77 (6.5%) missing valuesMissing
n_p_ecg_p_06 has 77 (6.5%) missing valuesMissing
n_p_ecg_p_07 has 77 (6.5%) missing valuesMissing
n_p_ecg_p_08 has 77 (6.5%) missing valuesMissing
n_p_ecg_p_09 has 77 (6.5%) missing valuesMissing
n_p_ecg_p_10 has 77 (6.5%) missing valuesMissing
n_p_ecg_p_11 has 77 (6.5%) missing valuesMissing
n_p_ecg_p_12 has 77 (6.5%) missing valuesMissing
GIPO_K has 228 (19.2%) missing valuesMissing
K_BLOOD has 229 (19.2%) missing valuesMissing
GIPER_NA has 232 (19.5%) missing valuesMissing
NA_BLOOD has 232 (19.5%) missing valuesMissing
ALT_BLOOD has 174 (14.6%) missing valuesMissing
AST_BLOOD has 175 (14.7%) missing valuesMissing
KFK_BLOOD has 1189 (99.9%) missing valuesMissing
L_BLOOD has 71 (6.0%) missing valuesMissing
ROE has 114 (9.6%) missing valuesMissing
TIME_B_S has 89 (7.5%) missing valuesMissing
NA_KB has 436 (36.6%) missing valuesMissing
NOT_NA_KB has 448 (37.6%) missing valuesMissing
LID_KB has 438 (36.8%) missing valuesMissing
GEPAR_S_n has 13 (1.1%) missing valuesMissing
ASP_S_n has 13 (1.1%) missing valuesMissing
STENOK_AN has 524 (44.0%) zerosZeros
DLIT_AG has 399 (33.5%) zerosZeros

Reproduction

Analysis started2024-05-17 09:14:43.273988
Analysis finished2024-05-17 09:16:02.452690
Duration1 minute and 19.18 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

AGE
Real number (ℝ)

Distinct61
Distinct (%)5.1%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean60.300758
Minimum26
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:02.634205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile41
Q153
median61
Q368
95-th percentile78
Maximum92
Range66
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.210903
Coefficient of variation (CV)0.18591645
Kurtosis-0.19154497
Mean60.300758
Median Absolute Deviation (MAD)8
Skewness-0.15108305
Sum71577
Variance125.68434
MonotonicityNot monotonic
2024-05-17T16:16:02.913979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 61
 
5.1%
62 59
 
5.0%
65 50
 
4.2%
52 47
 
3.9%
64 47
 
3.9%
70 43
 
3.6%
61 39
 
3.3%
55 38
 
3.2%
57 37
 
3.1%
59 36
 
3.0%
Other values (51) 730
61.3%
ValueCountFrequency (%)
26 1
 
0.1%
27 2
 
0.2%
30 1
 
0.1%
32 3
 
0.3%
33 2
 
0.2%
34 4
 
0.3%
35 5
 
0.4%
36 2
 
0.2%
37 13
1.1%
38 10
0.8%
ValueCountFrequency (%)
92 2
 
0.2%
88 4
 
0.3%
87 2
 
0.2%
86 1
 
0.1%
85 1
 
0.1%
84 3
 
0.3%
83 10
0.8%
82 4
 
0.3%
81 7
0.6%
80 11
0.9%

SEX
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size109.0 KiB
1
792 
0
398 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1190
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 792
66.6%
0 398
33.4%

Length

2024-05-17T16:16:03.161378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:03.302594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 792
66.6%
0 398
33.4%

Most occurring characters

ValueCountFrequency (%)
1 792
66.6%
0 398
33.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1190
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 792
66.6%
0 398
33.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1190
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 792
66.6%
0 398
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 792
66.6%
0 398
33.4%

INF_ANAM
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size111.3 KiB
0.0
778 
1.0
267 
2.0
94 
3.0
 
51

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 778
65.4%
1.0 267
 
22.4%
2.0 94
 
7.9%
3.0 51
 
4.3%

Length

2024-05-17T16:16:03.466615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:03.644576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 778
65.4%
1.0 267
 
22.4%
2.0 94
 
7.9%
3.0 51
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 1968
55.1%
. 1190
33.3%
1 267
 
7.5%
2 94
 
2.6%
3 51
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380
66.7%
Other Punctuation 1190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1968
82.7%
1 267
 
11.2%
2 94
 
3.9%
3 51
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1968
55.1%
. 1190
33.3%
1 267
 
7.5%
2 94
 
2.6%
3 51
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1968
55.1%
. 1190
33.3%
1 267
 
7.5%
2 94
 
2.6%
3 51
 
1.4%

STENOK_AN
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)0.6%
Missing45
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean2.0445415
Minimum0
Maximum6
Zeros524
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:03.802317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3653842
Coefficient of variation (CV)1.1569265
Kurtosis-1.1863696
Mean2.0445415
Median Absolute Deviation (MAD)1
Skewness0.67565062
Sum2341
Variance5.5950423
MonotonicityNot monotonic
2024-05-17T16:16:03.954487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 524
44.0%
6 199
 
16.7%
1 119
 
10.0%
2 93
 
7.8%
5 79
 
6.6%
3 77
 
6.5%
4 54
 
4.5%
(Missing) 45
 
3.8%
ValueCountFrequency (%)
0 524
44.0%
1 119
 
10.0%
2 93
 
7.8%
3 77
 
6.5%
4 54
 
4.5%
5 79
 
6.6%
6 199
 
16.7%
ValueCountFrequency (%)
6 199
 
16.7%
5 79
 
6.6%
4 54
 
4.5%
3 77
 
6.5%
2 93
 
7.8%
1 119
 
10.0%
0 524
44.0%

FK_STENOK
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.4%
Missing17
Missing (%)1.4%
Memory size111.2 KiB
2.0
569 
0.0
524 
1.0
 
40
3.0
 
29
4.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3519
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
2.0 569
47.8%
0.0 524
44.0%
1.0 40
 
3.4%
3.0 29
 
2.4%
4.0 11
 
0.9%
(Missing) 17
 
1.4%

Length

2024-05-17T16:16:04.142422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:04.310710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 569
48.5%
0.0 524
44.7%
1.0 40
 
3.4%
3.0 29
 
2.5%
4.0 11
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 1697
48.2%
. 1173
33.3%
2 569
 
16.2%
1 40
 
1.1%
3 29
 
0.8%
4 11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2346
66.7%
Other Punctuation 1173
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1697
72.3%
2 569
 
24.3%
1 40
 
1.7%
3 29
 
1.2%
4 11
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 1173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1697
48.2%
. 1173
33.3%
2 569
 
16.2%
1 40
 
1.1%
3 29
 
0.8%
4 11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1697
48.2%
. 1173
33.3%
2 569
 
16.2%
1 40
 
1.1%
3 29
 
0.8%
4 11
 
0.3%

IBS_POST
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing6
Missing (%)0.5%
Memory size111.3 KiB
2.0
468 
1.0
388 
0.0
328 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3552
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 468
39.3%
1.0 388
32.6%
0.0 328
27.6%
(Missing) 6
 
0.5%

Length

2024-05-17T16:16:04.489045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:04.654921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 468
39.5%
1.0 388
32.8%
0.0 328
27.7%

Most occurring characters

ValueCountFrequency (%)
0 1512
42.6%
. 1184
33.3%
2 468
 
13.2%
1 388
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2368
66.7%
Other Punctuation 1184
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1512
63.9%
2 468
 
19.8%
1 388
 
16.4%
Other Punctuation
ValueCountFrequency (%)
. 1184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1512
42.6%
. 1184
33.3%
2 468
 
13.2%
1 388
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1512
42.6%
. 1184
33.3%
2 468
 
13.2%
1 388
 
10.9%

IBS_NASL
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)3.0%
Missing1124
Missing (%)94.5%
Memory size106.9 KiB
0.0
40 
1.0
26 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters198
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 40
 
3.4%
1.0 26
 
2.2%
(Missing) 1124
94.5%

Length

2024-05-17T16:16:04.821023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:04.965195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 40
60.6%
1.0 26
39.4%

Most occurring characters

ValueCountFrequency (%)
0 106
53.5%
. 66
33.3%
1 26
 
13.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132
66.7%
Other Punctuation 66
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106
80.3%
1 26
 
19.7%
Other Punctuation
ValueCountFrequency (%)
. 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 198
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106
53.5%
. 66
33.3%
1 26
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106
53.5%
. 66
33.3%
1 26
 
13.1%

GB
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size111.3 KiB
2.0
626 
0.0
439 
3.0
119 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row0.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 626
52.6%
0.0 439
36.9%
3.0 119
 
10.0%
1.0 6
 
0.5%

Length

2024-05-17T16:16:05.130409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:05.308200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 626
52.6%
0.0 439
36.9%
3.0 119
 
10.0%
1.0 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1629
45.6%
. 1190
33.3%
2 626
 
17.5%
3 119
 
3.3%
1 6
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380
66.7%
Other Punctuation 1190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1629
68.4%
2 626
 
26.3%
3 119
 
5.0%
1 6
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1629
45.6%
. 1190
33.3%
2 626
 
17.5%
3 119
 
3.3%
1 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1629
45.6%
. 1190
33.3%
2 626
 
17.5%
3 119
 
3.3%
1 6
 
0.2%

SIM_GIPERT
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size111.3 KiB
0.0
1151 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1151
96.7%
1.0 39
 
3.3%

Length

2024-05-17T16:16:05.493056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:05.632925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1151
96.7%
1.0 39
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 2341
65.6%
. 1190
33.3%
1 39
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380
66.7%
Other Punctuation 1190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2341
98.4%
1 39
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2341
65.6%
. 1190
33.3%
1 39
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2341
65.6%
. 1190
33.3%
1 39
 
1.1%

DLIT_AG
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)0.8%
Missing148
Missing (%)12.4%
Infinite0
Infinite (%)0.0%
Mean3.1602687
Minimum0
Maximum7
Zeros399
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:05.769509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.0399573
Coefficient of variation (CV)0.96192999
Kurtosis-1.7714216
Mean3.1602687
Median Absolute Deviation (MAD)2
Skewness0.1722994
Sum3293
Variance9.2413402
MonotonicityNot monotonic
2024-05-17T16:16:05.926185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 399
33.5%
7 273
22.9%
6 118
 
9.9%
1 82
 
6.9%
5 58
 
4.9%
2 50
 
4.2%
3 46
 
3.9%
4 16
 
1.3%
(Missing) 148
 
12.4%
ValueCountFrequency (%)
0 399
33.5%
1 82
 
6.9%
2 50
 
4.2%
3 46
 
3.9%
4 16
 
1.3%
5 58
 
4.9%
6 118
 
9.9%
7 273
22.9%
ValueCountFrequency (%)
7 273
22.9%
6 118
 
9.9%
5 58
 
4.9%
4 16
 
1.3%
3 46
 
3.9%
2 50
 
4.2%
1 82
 
6.9%
0 399
33.5%

ZSN_A
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing1
Missing (%)0.1%
Memory size111.3 KiB
0.0
1061 
1.0
 
98
3.0
 
16
2.0
 
8
4.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3567
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1061
89.2%
1.0 98
 
8.2%
3.0 16
 
1.3%
2.0 8
 
0.7%
4.0 6
 
0.5%
(Missing) 1
 
0.1%

Length

2024-05-17T16:16:06.128172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:06.308480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1061
89.2%
1.0 98
 
8.2%
3.0 16
 
1.3%
2.0 8
 
0.7%
4.0 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 2250
63.1%
. 1189
33.3%
1 98
 
2.7%
3 16
 
0.4%
2 8
 
0.2%
4 6
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2378
66.7%
Other Punctuation 1189
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2250
94.6%
1 98
 
4.1%
3 16
 
0.7%
2 8
 
0.3%
4 6
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 1189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3567
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2250
63.1%
. 1189
33.3%
1 98
 
2.7%
3 16
 
0.4%
2 8
 
0.2%
4 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2250
63.1%
. 1189
33.3%
1 98
 
2.7%
3 16
 
0.4%
2 8
 
0.2%
4 6
 
0.2%

nr_11
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size111.3 KiB
0.0
1154 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3564
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1154
97.0%
1.0 34
 
2.9%
(Missing) 2
 
0.2%

Length

2024-05-17T16:16:06.487879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:06.655239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1154
97.1%
1.0 34
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 2342
65.7%
. 1188
33.3%
1 34
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2376
66.7%
Other Punctuation 1188
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2342
98.6%
1 34
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2342
65.7%
. 1188
33.3%
1 34
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2342
65.7%
. 1188
33.3%
1 34
 
1.0%

nr_01
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size111.3 KiB
0.0
1186 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3564
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1186
99.7%
1.0 2
 
0.2%
(Missing) 2
 
0.2%

Length

2024-05-17T16:16:06.819326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:06.964023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1186
99.8%
1.0 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 2374
66.6%
. 1188
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2376
66.7%
Other Punctuation 1188
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2374
99.9%
1 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2374
66.6%
. 1188
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2374
66.6%
. 1188
33.3%
1 2
 
0.1%

nr_02
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size111.3 KiB
0.0
1173 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3564
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1173
98.6%
1.0 15
 
1.3%
(Missing) 2
 
0.2%

Length

2024-05-17T16:16:07.122674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:07.269766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1173
98.7%
1.0 15
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 2361
66.2%
. 1188
33.3%
1 15
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2376
66.7%
Other Punctuation 1188
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2361
99.4%
1 15
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2361
66.2%
. 1188
33.3%
1 15
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2361
66.2%
. 1188
33.3%
1 15
 
0.4%

nr_03
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size111.3 KiB
0.0
1167 
1.0
 
21

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3564
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1167
98.1%
1.0 21
 
1.8%
(Missing) 2
 
0.2%

Length

2024-05-17T16:16:07.416303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:07.577967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1167
98.2%
1.0 21
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 2355
66.1%
. 1188
33.3%
1 21
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2376
66.7%
Other Punctuation 1188
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2355
99.1%
1 21
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2355
66.1%
. 1188
33.3%
1 21
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2355
66.1%
. 1188
33.3%
1 21
 
0.6%

nr_04
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size111.3 KiB
0.0
1174 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3564
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1174
98.7%
1.0 14
 
1.2%
(Missing) 2
 
0.2%

Length

2024-05-17T16:16:07.729596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:07.872428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1174
98.8%
1.0 14
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 2362
66.3%
. 1188
33.3%
1 14
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2376
66.7%
Other Punctuation 1188
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2362
99.4%
1 14
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2362
66.3%
. 1188
33.3%
1 14
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2362
66.3%
. 1188
33.3%
1 14
 
0.4%

nr_07
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size111.3 KiB
0.0
1187 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3564
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1187
99.7%
1.0 1
 
0.1%
(Missing) 2
 
0.2%

Length

2024-05-17T16:16:08.022913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:08.173092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1187
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2375
66.6%
. 1188
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2376
66.7%
Other Punctuation 1188
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2375
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2375
66.6%
. 1188
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2375
66.6%
. 1188
33.3%
1 1
 
< 0.1%

nr_08
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size111.3 KiB
0.0
1186 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3564
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1186
99.7%
1.0 2
 
0.2%
(Missing) 2
 
0.2%

Length

2024-05-17T16:16:08.906154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:09.048419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1186
99.8%
1.0 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 2374
66.6%
. 1188
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2376
66.7%
Other Punctuation 1188
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2374
99.9%
1 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2374
66.6%
. 1188
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2374
66.6%
. 1188
33.3%
1 2
 
0.1%

np_01
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size111.3 KiB
0.0
1189 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1189
99.9%
1.0 1
 
0.1%

Length

2024-05-17T16:16:09.208905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:09.350064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1189
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2379
66.6%
. 1190
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380
66.7%
Other Punctuation 1190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2379
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2379
66.6%
. 1190
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2379
66.6%
. 1190
33.3%
1 1
 
< 0.1%

np_04
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size111.3 KiB
0.0
1188 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1188
99.8%
1.0 2
 
0.2%

Length

2024-05-17T16:16:09.509982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:09.655055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1188
99.8%
1.0 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 2378
66.6%
. 1190
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380
66.7%
Other Punctuation 1190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2378
99.9%
1 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2378
66.6%
. 1190
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2378
66.6%
. 1190
33.3%
1 2
 
0.1%

np_05
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size111.3 KiB
0.0
1180 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1180
99.2%
1.0 10
 
0.8%

Length

2024-05-17T16:16:09.814995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:09.954313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1180
99.2%
1.0 10
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 2370
66.4%
. 1190
33.3%
1 10
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380
66.7%
Other Punctuation 1190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2370
99.6%
1 10
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2370
66.4%
. 1190
33.3%
1 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2370
66.4%
. 1190
33.3%
1 10
 
0.3%

np_07
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size111.3 KiB
0.0
1189 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1189
99.9%
1.0 1
 
0.1%

Length

2024-05-17T16:16:10.112670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:10.253854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1189
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2379
66.6%
. 1190
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380
66.7%
Other Punctuation 1190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2379
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2379
66.6%
. 1190
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2379
66.6%
. 1190
33.3%
1 1
 
< 0.1%

np_08
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size111.3 KiB
0.0
1187 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1187
99.7%
1.0 3
 
0.3%

Length

2024-05-17T16:16:10.415496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:10.555825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1187
99.7%
1.0 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 2377
66.6%
. 1190
33.3%
1 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380
66.7%
Other Punctuation 1190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2377
99.9%
1 3
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2377
66.6%
. 1190
33.3%
1 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2377
66.6%
. 1190
33.3%
1 3
 
0.1%

np_09
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size111.3 KiB
0.0
1188 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1188
99.8%
1.0 2
 
0.2%

Length

2024-05-17T16:16:10.708961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:10.864940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1188
99.8%
1.0 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 2378
66.6%
. 1190
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380
66.7%
Other Punctuation 1190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2378
99.9%
1 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2378
66.6%
. 1190
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2378
66.6%
. 1190
33.3%
1 2
 
0.1%

np_10
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size111.3 KiB
0.0
1189 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1189
99.9%
1.0 1
 
0.1%

Length

2024-05-17T16:16:11.024110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:11.175313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1189
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2379
66.6%
. 1190
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380
66.7%
Other Punctuation 1190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2379
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2379
66.6%
. 1190
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2379
66.6%
. 1190
33.3%
1 1
 
< 0.1%

endocr_01
Categorical

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size111.3 KiB
0.0
1052 
1.0
137 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3567
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1052
88.4%
1.0 137
 
11.5%
(Missing) 1
 
0.1%

Length

2024-05-17T16:16:11.325048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:11.503156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1052
88.5%
1.0 137
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 2241
62.8%
. 1189
33.3%
1 137
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2378
66.7%
Other Punctuation 1189
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2241
94.2%
1 137
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 1189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3567
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2241
62.8%
. 1189
33.3%
1 137
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2241
62.8%
. 1189
33.3%
1 137
 
3.8%

endocr_02
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size111.3 KiB
0.0
1167 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3567
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1167
98.1%
1.0 22
 
1.8%
(Missing) 1
 
0.1%

Length

2024-05-17T16:16:11.662795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:11.808879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1167
98.1%
1.0 22
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 2356
66.0%
. 1189
33.3%
1 22
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2378
66.7%
Other Punctuation 1189
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2356
99.1%
1 22
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 1189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3567
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2356
66.0%
. 1189
33.3%
1 22
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2356
66.0%
. 1189
33.3%
1 22
 
0.6%

endocr_03
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size111.3 KiB
0.0
1179 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3567
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1179
99.1%
1.0 10
 
0.8%
(Missing) 1
 
0.1%

Length

2024-05-17T16:16:11.962635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:12.115059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1179
99.2%
1.0 10
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 2368
66.4%
. 1189
33.3%
1 10
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2378
66.7%
Other Punctuation 1189
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2368
99.6%
1 10
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 1189
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3567
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2368
66.4%
. 1189
33.3%
1 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2368
66.4%
. 1189
33.3%
1 10
 
0.3%

zab_leg_01
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size111.3 KiB
0.0
1066 
1.0
122 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3564
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1066
89.6%
1.0 122
 
10.3%
(Missing) 2
 
0.2%

Length

2024-05-17T16:16:12.265254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:12.408776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1066
89.7%
1.0 122
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0 2254
63.2%
. 1188
33.3%
1 122
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2376
66.7%
Other Punctuation 1188
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2254
94.9%
1 122
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2254
63.2%
. 1188
33.3%
1 122
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2254
63.2%
. 1188
33.3%
1 122
 
3.4%

zab_leg_02
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size111.3 KiB
0.0
1130 
1.0
 
58

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3564
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1130
95.0%
1.0 58
 
4.9%
(Missing) 2
 
0.2%

Length

2024-05-17T16:16:12.564201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:12.704872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1130
95.1%
1.0 58
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 2318
65.0%
. 1188
33.3%
1 58
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2376
66.7%
Other Punctuation 1188
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2318
97.6%
1 58
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2318
65.0%
. 1188
33.3%
1 58
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2318
65.0%
. 1188
33.3%
1 58
 
1.6%

zab_leg_03
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size111.3 KiB
0.0
1174 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3564
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1174
98.7%
1.0 14
 
1.2%
(Missing) 2
 
0.2%

Length

2024-05-17T16:16:12.857979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:12.994162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1174
98.8%
1.0 14
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 2362
66.3%
. 1188
33.3%
1 14
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2376
66.7%
Other Punctuation 1188
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2362
99.4%
1 14
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2362
66.3%
. 1188
33.3%
1 14
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2362
66.3%
. 1188
33.3%
1 14
 
0.4%

zab_leg_04
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size111.3 KiB
0.0
1185 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3564
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1185
99.6%
1.0 3
 
0.3%
(Missing) 2
 
0.2%

Length

2024-05-17T16:16:13.153340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:13.319273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1185
99.7%
1.0 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 2373
66.6%
. 1188
33.3%
1 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2376
66.7%
Other Punctuation 1188
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2373
99.9%
1 3
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2373
66.6%
. 1188
33.3%
1 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2373
66.6%
. 1188
33.3%
1 3
 
0.1%

zab_leg_06
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size111.3 KiB
0.0
1169 
1.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3564
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1169
98.2%
1.0 19
 
1.6%
(Missing) 2
 
0.2%

Length

2024-05-17T16:16:13.476962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:13.616146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1169
98.4%
1.0 19
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 2357
66.1%
. 1188
33.3%
1 19
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2376
66.7%
Other Punctuation 1188
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2357
99.2%
1 19
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 1188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2357
66.1%
. 1188
33.3%
1 19
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2357
66.1%
. 1188
33.3%
1 19
 
0.5%

S_AD_KBRIG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)6.0%
Missing760
Missing (%)63.9%
Infinite0
Infinite (%)0.0%
Mean141.65116
Minimum50
Maximum260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:13.768485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile90
Q1120
median140
Q3160
95-th percentile200
Maximum260
Range210
Interquartile range (IQR)40

Descriptive statistics

Standard deviation31.866112
Coefficient of variation (CV)0.22496188
Kurtosis0.53987686
Mean141.65116
Median Absolute Deviation (MAD)20
Skewness0.3691551
Sum60910
Variance1015.4491
MonotonicityNot monotonic
2024-05-17T16:16:13.986107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
140 65
 
5.5%
160 61
 
5.1%
130 53
 
4.5%
120 45
 
3.8%
110 31
 
2.6%
150 30
 
2.5%
180 24
 
2.0%
170 19
 
1.6%
100 19
 
1.6%
90 14
 
1.2%
Other values (16) 69
 
5.8%
(Missing) 760
63.9%
ValueCountFrequency (%)
50 1
 
0.1%
60 3
 
0.3%
80 8
 
0.7%
90 14
 
1.2%
100 19
1.6%
105 2
 
0.2%
110 31
2.6%
115 4
 
0.3%
120 45
3.8%
125 5
 
0.4%
ValueCountFrequency (%)
260 1
 
0.1%
240 1
 
0.1%
230 1
 
0.1%
220 8
 
0.7%
210 7
 
0.6%
200 8
 
0.7%
190 11
 
0.9%
180 24
 
2.0%
170 19
 
1.6%
160 61
5.1%

D_AD_KBRIG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)4.2%
Missing760
Missing (%)63.9%
Infinite0
Infinite (%)0.0%
Mean84.27907
Minimum0
Maximum190
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:14.156107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q180
median80
Q390
95-th percentile110
Maximum190
Range190
Interquartile range (IQR)10

Descriptive statistics

Standard deviation16.771918
Coefficient of variation (CV)0.19900454
Kurtosis6.3399181
Mean84.27907
Median Absolute Deviation (MAD)10
Skewness0.2490164
Sum36240
Variance281.29723
MonotonicityNot monotonic
2024-05-17T16:16:14.344518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
90 120
 
10.1%
80 114
 
9.6%
100 63
 
5.3%
70 57
 
4.8%
60 35
 
2.9%
110 13
 
1.1%
120 10
 
0.8%
85 4
 
0.3%
50 3
 
0.3%
40 3
 
0.3%
Other values (8) 8
 
0.7%
(Missing) 760
63.9%
ValueCountFrequency (%)
0 1
 
0.1%
10 1
 
0.1%
30 1
 
0.1%
40 3
 
0.3%
50 3
 
0.3%
60 35
 
2.9%
65 1
 
0.1%
70 57
4.8%
80 114
9.6%
85 4
 
0.3%
ValueCountFrequency (%)
190 1
 
0.1%
160 1
 
0.1%
140 1
 
0.1%
120 10
 
0.8%
110 13
 
1.1%
100 63
5.3%
95 1
 
0.1%
90 120
10.1%
85 4
 
0.3%
80 114
9.6%

S_AD_ORIT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)3.0%
Missing249
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean138.9745
Minimum60
Maximum260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:14.532520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile100
Q1120
median140
Q3160
95-th percentile190
Maximum260
Range200
Interquartile range (IQR)40

Descriptive statistics

Standard deviation27.325566
Coefficient of variation (CV)0.19662289
Kurtosis0.9231069
Mean138.9745
Median Absolute Deviation (MAD)20
Skewness0.54096027
Sum130775
Variance746.68658
MonotonicityNot monotonic
2024-05-17T16:16:14.739405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
130 167
14.0%
120 157
13.2%
140 146
12.3%
160 113
9.5%
150 75
 
6.3%
110 64
 
5.4%
180 46
 
3.9%
170 33
 
2.8%
100 32
 
2.7%
200 22
 
1.8%
Other values (18) 86
 
7.2%
(Missing) 249
20.9%
ValueCountFrequency (%)
60 4
 
0.3%
70 4
 
0.3%
80 6
 
0.5%
90 20
 
1.7%
95 1
 
0.1%
100 32
 
2.7%
105 4
 
0.3%
110 64
5.4%
115 5
 
0.4%
120 157
13.2%
ValueCountFrequency (%)
260 1
 
0.1%
240 1
 
0.1%
230 2
 
0.2%
220 6
 
0.5%
210 5
 
0.4%
200 22
1.8%
195 1
 
0.1%
190 18
 
1.5%
180 46
3.9%
170 33
2.8%

D_AD_ORIT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)1.7%
Missing249
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean85.478215
Minimum40
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:14.920545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile60
Q180
median80
Q390
95-th percentile110
Maximum190
Range150
Interquartile range (IQR)10

Descriptive statistics

Standard deviation14.510994
Coefficient of variation (CV)0.16976248
Kurtosis3.5778833
Mean85.478215
Median Absolute Deviation (MAD)10
Skewness0.55037046
Sum80435
Variance210.56894
MonotonicityNot monotonic
2024-05-17T16:16:15.095458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
80 337
28.3%
90 220
18.5%
100 147
12.4%
70 96
 
8.1%
60 54
 
4.5%
110 37
 
3.1%
120 18
 
1.5%
40 7
 
0.6%
95 6
 
0.5%
130 6
 
0.5%
Other values (6) 13
 
1.1%
(Missing) 249
20.9%
ValueCountFrequency (%)
40 7
 
0.6%
50 5
 
0.4%
60 54
 
4.5%
70 96
 
8.1%
75 1
 
0.1%
80 337
28.3%
85 3
 
0.3%
90 220
18.5%
95 6
 
0.5%
100 147
12.4%
ValueCountFrequency (%)
190 1
 
0.1%
140 2
 
0.2%
130 6
 
0.5%
120 18
 
1.5%
110 37
 
3.1%
105 1
 
0.1%
100 147
12.4%
95 6
 
0.5%
90 220
18.5%
85 3
 
0.3%

O_L_POST
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing11
Missing (%)0.9%
Memory size111.3 KiB
0.0
1127 
1.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1127
94.7%
1.0 52
 
4.4%
(Missing) 11
 
0.9%

Length

2024-05-17T16:16:15.293218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:15.449388image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1127
95.6%
1.0 52
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 2306
65.2%
. 1179
33.3%
1 52
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2358
66.7%
Other Punctuation 1179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2306
97.8%
1 52
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 1179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2306
65.2%
. 1179
33.3%
1 52
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2306
65.2%
. 1179
33.3%
1 52
 
1.5%

K_SH_POST
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing12
Missing (%)1.0%
Memory size111.3 KiB
0.0
1176 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3534
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1176
98.8%
1.0 2
 
0.2%
(Missing) 12
 
1.0%

Length

2024-05-17T16:16:15.597270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:15.736223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1176
99.8%
1.0 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 2354
66.6%
. 1178
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2356
66.7%
Other Punctuation 1178
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2354
99.9%
1 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2354
66.6%
. 1178
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2354
66.6%
. 1178
33.3%
1 2
 
0.1%

MP_TP_POST
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing11
Missing (%)0.9%
Memory size111.3 KiB
0.0
1126 
1.0
 
53

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1126
94.6%
1.0 53
 
4.5%
(Missing) 11
 
0.9%

Length

2024-05-17T16:16:15.893343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:16.037165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1126
95.5%
1.0 53
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 2305
65.2%
. 1179
33.3%
1 53
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2358
66.7%
Other Punctuation 1179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2305
97.8%
1 53
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 1179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2305
65.2%
. 1179
33.3%
1 53
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2305
65.2%
. 1179
33.3%
1 53
 
1.5%

SVT_POST
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing11
Missing (%)0.9%
Memory size111.3 KiB
0.0
1173 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1173
98.6%
1.0 6
 
0.5%
(Missing) 11
 
0.9%

Length

2024-05-17T16:16:16.197624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:16.336781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1173
99.5%
1.0 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 2352
66.5%
. 1179
33.3%
1 6
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2358
66.7%
Other Punctuation 1179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2352
99.7%
1 6
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 1179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2352
66.5%
. 1179
33.3%
1 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2352
66.5%
. 1179
33.3%
1 6
 
0.2%

GT_POST
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing11
Missing (%)0.9%
Memory size111.3 KiB
0.0
1174 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1174
98.7%
1.0 5
 
0.4%
(Missing) 11
 
0.9%

Length

2024-05-17T16:16:16.491333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:16.634852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1174
99.6%
1.0 5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 2353
66.5%
. 1179
33.3%
1 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2358
66.7%
Other Punctuation 1179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2353
99.8%
1 5
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 1179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2353
66.5%
. 1179
33.3%
1 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2353
66.5%
. 1179
33.3%
1 5
 
0.1%

FIB_G_POST
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing11
Missing (%)0.9%
Memory size111.3 KiB
0.0
1172 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1172
98.5%
1.0 7
 
0.6%
(Missing) 11
 
0.9%

Length

2024-05-17T16:16:16.797606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:16.937606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1172
99.4%
1.0 7
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 2351
66.5%
. 1179
33.3%
1 7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2358
66.7%
Other Punctuation 1179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2351
99.7%
1 7
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 1179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2351
66.5%
. 1179
33.3%
1 7
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2351
66.5%
. 1179
33.3%
1 7
 
0.2%

ant_im
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.4%
Missing29
Missing (%)2.4%
Memory size111.2 KiB
0.0
465 
1.0
328 
4.0
314 
2.0
 
34
3.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3483
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row4.0
4th row0.0
5th row4.0

Common Values

ValueCountFrequency (%)
0.0 465
39.1%
1.0 328
27.6%
4.0 314
26.4%
2.0 34
 
2.9%
3.0 20
 
1.7%
(Missing) 29
 
2.4%

Length

2024-05-17T16:16:17.089605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:17.258675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 465
40.1%
1.0 328
28.3%
4.0 314
27.0%
2.0 34
 
2.9%
3.0 20
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1626
46.7%
. 1161
33.3%
1 328
 
9.4%
4 314
 
9.0%
2 34
 
1.0%
3 20
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2322
66.7%
Other Punctuation 1161
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1626
70.0%
1 328
 
14.1%
4 314
 
13.5%
2 34
 
1.5%
3 20
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 1161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1626
46.7%
. 1161
33.3%
1 328
 
9.4%
4 314
 
9.0%
2 34
 
1.0%
3 20
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1626
46.7%
. 1161
33.3%
1 328
 
9.4%
4 314
 
9.0%
2 34
 
1.0%
3 20
 
0.6%

lat_im
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.4%
Missing24
Missing (%)2.0%
Memory size111.2 KiB
1.0
621 
0.0
418 
2.0
 
57
3.0
 
49
4.0
 
21

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3498
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 621
52.2%
0.0 418
35.1%
2.0 57
 
4.8%
3.0 49
 
4.1%
4.0 21
 
1.8%
(Missing) 24
 
2.0%

Length

2024-05-17T16:16:17.443725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:17.607556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 621
53.3%
0.0 418
35.8%
2.0 57
 
4.9%
3.0 49
 
4.2%
4.0 21
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 1584
45.3%
. 1166
33.3%
1 621
 
17.8%
2 57
 
1.6%
3 49
 
1.4%
4 21
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2332
66.7%
Other Punctuation 1166
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1584
67.9%
1 621
 
26.6%
2 57
 
2.4%
3 49
 
2.1%
4 21
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 1166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3498
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1584
45.3%
. 1166
33.3%
1 621
 
17.8%
2 57
 
1.6%
3 49
 
1.4%
4 21
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1584
45.3%
. 1166
33.3%
1 621
 
17.8%
2 57
 
1.6%
3 49
 
1.4%
4 21
 
0.6%

inf_im
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.4%
Missing23
Missing (%)1.9%
Memory size111.2 KiB
0.0
686 
1.0
156 
2.0
134 
4.0
107 
3.0
84 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3501
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 686
57.6%
1.0 156
 
13.1%
2.0 134
 
11.3%
4.0 107
 
9.0%
3.0 84
 
7.1%
(Missing) 23
 
1.9%

Length

2024-05-17T16:16:17.779929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:17.944996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 686
58.8%
1.0 156
 
13.4%
2.0 134
 
11.5%
4.0 107
 
9.2%
3.0 84
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 1853
52.9%
. 1167
33.3%
1 156
 
4.5%
2 134
 
3.8%
4 107
 
3.1%
3 84
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2334
66.7%
Other Punctuation 1167
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1853
79.4%
1 156
 
6.7%
2 134
 
5.7%
4 107
 
4.6%
3 84
 
3.6%
Other Punctuation
ValueCountFrequency (%)
. 1167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3501
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1853
52.9%
. 1167
33.3%
1 156
 
4.5%
2 134
 
3.8%
4 107
 
3.1%
3 84
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3501
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1853
52.9%
. 1167
33.3%
1 156
 
4.5%
2 134
 
3.8%
4 107
 
3.1%
3 84
 
2.4%

post_im
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)0.4%
Missing22
Missing (%)1.8%
Memory size111.2 KiB
0.0
975 
1.0
117 
2.0
 
37
3.0
 
29
4.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3504
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 975
81.9%
1.0 117
 
9.8%
2.0 37
 
3.1%
3.0 29
 
2.4%
4.0 10
 
0.8%
(Missing) 22
 
1.8%

Length

2024-05-17T16:16:18.124557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:18.325379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 975
83.5%
1.0 117
 
10.0%
2.0 37
 
3.2%
3.0 29
 
2.5%
4.0 10
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 2143
61.2%
. 1168
33.3%
1 117
 
3.3%
2 37
 
1.1%
3 29
 
0.8%
4 10
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2336
66.7%
Other Punctuation 1168
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2143
91.7%
1 117
 
5.0%
2 37
 
1.6%
3 29
 
1.2%
4 10
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 1168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3504
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2143
61.2%
. 1168
33.3%
1 117
 
3.3%
2 37
 
1.1%
3 29
 
0.8%
4 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2143
61.2%
. 1168
33.3%
1 117
 
3.3%
2 37
 
1.1%
3 29
 
0.8%
4 10
 
0.3%

IM_PG_P
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size111.3 KiB
0.0
1166 
1.0
 
24

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3570
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1166
98.0%
1.0 24
 
2.0%

Length

2024-05-17T16:16:18.498563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:18.645374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1166
98.0%
1.0 24
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 2356
66.0%
. 1190
33.3%
1 24
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2380
66.7%
Other Punctuation 1190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2356
99.0%
1 24
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2356
66.0%
. 1190
33.3%
1 24
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2356
66.0%
. 1190
33.3%
1 24
 
0.7%

ritm_ecg_p_01
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing112
Missing (%)9.4%
Memory size110.9 KiB
1.0
775 
0.0
303 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3234
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 775
65.1%
0.0 303
 
25.5%
(Missing) 112
 
9.4%

Length

2024-05-17T16:16:18.798411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:18.938268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 775
71.9%
0.0 303
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 1381
42.7%
. 1078
33.3%
1 775
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2156
66.7%
Other Punctuation 1078
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1381
64.1%
1 775
35.9%
Other Punctuation
ValueCountFrequency (%)
. 1078
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1381
42.7%
. 1078
33.3%
1 775
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1381
42.7%
. 1078
33.3%
1 775
24.0%

ritm_ecg_p_02
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing112
Missing (%)9.4%
Memory size110.9 KiB
0.0
1034 
1.0
 
44

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3234
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1034
86.9%
1.0 44
 
3.7%
(Missing) 112
 
9.4%

Length

2024-05-17T16:16:19.098526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:19.251638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1034
95.9%
1.0 44
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 2112
65.3%
. 1078
33.3%
1 44
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2156
66.7%
Other Punctuation 1078
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2112
98.0%
1 44
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 1078
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2112
65.3%
. 1078
33.3%
1 44
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2112
65.3%
. 1078
33.3%
1 44
 
1.4%

ritm_ecg_p_04
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing112
Missing (%)9.4%
Memory size110.9 KiB
0.0
1071 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3234
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1071
90.0%
1.0 7
 
0.6%
(Missing) 112
 
9.4%

Length

2024-05-17T16:16:19.408592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:19.549929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1071
99.4%
1.0 7
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 2149
66.5%
. 1078
33.3%
1 7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2156
66.7%
Other Punctuation 1078
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2149
99.7%
1 7
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 1078
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2149
66.5%
. 1078
33.3%
1 7
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2149
66.5%
. 1078
33.3%
1 7
 
0.2%

ritm_ecg_p_06
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing112
Missing (%)9.4%
Memory size110.9 KiB
0.0
1077 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3234
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1077
90.5%
1.0 1
 
0.1%
(Missing) 112
 
9.4%

Length

2024-05-17T16:16:19.705443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:19.850333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1077
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2155
66.6%
. 1078
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2156
66.7%
Other Punctuation 1078
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2155
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1078
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2155
66.6%
. 1078
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2155
66.6%
. 1078
33.3%
1 1
 
< 0.1%

ritm_ecg_p_07
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing112
Missing (%)9.4%
Memory size110.9 KiB
0.0
861 
1.0
217 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3234
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 861
72.4%
1.0 217
 
18.2%
(Missing) 112
 
9.4%

Length

2024-05-17T16:16:20.003290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:20.146254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 861
79.9%
1.0 217
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 1939
60.0%
. 1078
33.3%
1 217
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2156
66.7%
Other Punctuation 1078
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1939
89.9%
1 217
 
10.1%
Other Punctuation
ValueCountFrequency (%)
. 1078
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1939
60.0%
. 1078
33.3%
1 217
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1939
60.0%
. 1078
33.3%
1 217
 
6.7%

ritm_ecg_p_08
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing112
Missing (%)9.4%
Memory size110.9 KiB
0.0
1044 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3234
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1044
87.7%
1.0 34
 
2.9%
(Missing) 112
 
9.4%

Length

2024-05-17T16:16:20.312367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:20.447174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1044
96.8%
1.0 34
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 2122
65.6%
. 1078
33.3%
1 34
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2156
66.7%
Other Punctuation 1078
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2122
98.4%
1 34
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 1078
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2122
65.6%
. 1078
33.3%
1 34
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2122
65.6%
. 1078
33.3%
1 34
 
1.1%

n_r_ecg_p_01
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing76
Missing (%)6.4%
Memory size111.0 KiB
0.0
1073 
1.0
 
41

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3342
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1073
90.2%
1.0 41
 
3.4%
(Missing) 76
 
6.4%

Length

2024-05-17T16:16:20.596920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:20.739265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1073
96.3%
1.0 41
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 2187
65.4%
. 1114
33.3%
1 41
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2228
66.7%
Other Punctuation 1114
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2187
98.2%
1 41
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 1114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2187
65.4%
. 1114
33.3%
1 41
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2187
65.4%
. 1114
33.3%
1 41
 
1.2%

n_r_ecg_p_02
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing76
Missing (%)6.4%
Memory size111.0 KiB
0.0
1109 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3342
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1109
93.2%
1.0 5
 
0.4%
(Missing) 76
 
6.4%

Length

2024-05-17T16:16:20.899218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:21.047258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1109
99.6%
1.0 5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 2223
66.5%
. 1114
33.3%
1 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2228
66.7%
Other Punctuation 1114
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2223
99.8%
1 5
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 1114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2223
66.5%
. 1114
33.3%
1 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2223
66.5%
. 1114
33.3%
1 5
 
0.1%

n_r_ecg_p_03
Categorical

MISSING 

Distinct2
Distinct (%)0.2%
Missing76
Missing (%)6.4%
Memory size111.0 KiB
0.0
959 
1.0
155 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3342
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 959
80.6%
1.0 155
 
13.0%
(Missing) 76
 
6.4%

Length

2024-05-17T16:16:21.211810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:21.358380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 959
86.1%
1.0 155
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 2073
62.0%
. 1114
33.3%
1 155
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2228
66.7%
Other Punctuation 1114
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2073
93.0%
1 155
 
7.0%
Other Punctuation
ValueCountFrequency (%)
. 1114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2073
62.0%
. 1114
33.3%
1 155
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2073
62.0%
. 1114
33.3%
1 155
 
4.6%

n_r_ecg_p_04
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing76
Missing (%)6.4%
Memory size111.0 KiB
0.0
1065 
1.0
 
49

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3342
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1065
89.5%
1.0 49
 
4.1%
(Missing) 76
 
6.4%

Length

2024-05-17T16:16:21.510037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:21.651890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1065
95.6%
1.0 49
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 2179
65.2%
. 1114
33.3%
1 49
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2228
66.7%
Other Punctuation 1114
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2179
97.8%
1 49
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 1114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2179
65.2%
. 1114
33.3%
1 49
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2179
65.2%
. 1114
33.3%
1 49
 
1.5%

n_r_ecg_p_05
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing76
Missing (%)6.4%
Memory size111.0 KiB
0.0
1084 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3342
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1084
91.1%
1.0 30
 
2.5%
(Missing) 76
 
6.4%

Length

2024-05-17T16:16:21.803661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:21.946221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1084
97.3%
1.0 30
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 2198
65.8%
. 1114
33.3%
1 30
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2228
66.7%
Other Punctuation 1114
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2198
98.7%
1 30
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 1114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2198
65.8%
. 1114
33.3%
1 30
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2198
65.8%
. 1114
33.3%
1 30
 
0.9%

n_r_ecg_p_06
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing76
Missing (%)6.4%
Memory size111.0 KiB
0.0
1098 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3342
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1098
92.3%
1.0 16
 
1.3%
(Missing) 76
 
6.4%

Length

2024-05-17T16:16:22.099437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:22.250077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1098
98.6%
1.0 16
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 2212
66.2%
. 1114
33.3%
1 16
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2228
66.7%
Other Punctuation 1114
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2212
99.3%
1 16
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 1114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2212
66.2%
. 1114
33.3%
1 16
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2212
66.2%
. 1114
33.3%
1 16
 
0.5%

n_r_ecg_p_08
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing76
Missing (%)6.4%
Memory size111.0 KiB
0.0
1111 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3342
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1111
93.4%
1.0 3
 
0.3%
(Missing) 76
 
6.4%

Length

2024-05-17T16:16:22.408143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:22.545762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1111
99.7%
1.0 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 2225
66.6%
. 1114
33.3%
1 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2228
66.7%
Other Punctuation 1114
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2225
99.9%
1 3
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2225
66.6%
. 1114
33.3%
1 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2225
66.6%
. 1114
33.3%
1 3
 
0.1%

n_r_ecg_p_09
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing76
Missing (%)6.4%
Memory size111.0 KiB
0.0
1113 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3342
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1113
93.5%
1.0 1
 
0.1%
(Missing) 76
 
6.4%

Length

2024-05-17T16:16:22.707739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:22.861458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1113
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2227
66.6%
. 1114
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2228
66.7%
Other Punctuation 1114
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2227
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2227
66.6%
. 1114
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2227
66.6%
. 1114
33.3%
1 1
 
< 0.1%

n_r_ecg_p_10
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing76
Missing (%)6.4%
Memory size111.0 KiB
0.0
1112 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3342
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1112
93.4%
1.0 2
 
0.2%
(Missing) 76
 
6.4%

Length

2024-05-17T16:16:23.020055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:23.166638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1112
99.8%
1.0 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 2226
66.6%
. 1114
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2228
66.7%
Other Punctuation 1114
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2226
99.9%
1 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2226
66.6%
. 1114
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2226
66.6%
. 1114
33.3%
1 2
 
0.1%

n_p_ecg_p_01
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing77
Missing (%)6.5%
Memory size111.0 KiB
0.0
1111 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3339
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1111
93.4%
1.0 2
 
0.2%
(Missing) 77
 
6.5%

Length

2024-05-17T16:16:23.338072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:23.477514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1111
99.8%
1.0 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 2224
66.6%
. 1113
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2226
66.7%
Other Punctuation 1113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2224
99.9%
1 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2224
66.6%
. 1113
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2224
66.6%
. 1113
33.3%
1 2
 
0.1%

n_p_ecg_p_03
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing77
Missing (%)6.5%
Memory size111.0 KiB
0.0
1097 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3339
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1097
92.2%
1.0 16
 
1.3%
(Missing) 77
 
6.5%

Length

2024-05-17T16:16:23.624489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:23.767558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1097
98.6%
1.0 16
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 2210
66.2%
. 1113
33.3%
1 16
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2226
66.7%
Other Punctuation 1113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2210
99.3%
1 16
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2210
66.2%
. 1113
33.3%
1 16
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2210
66.2%
. 1113
33.3%
1 16
 
0.5%

n_p_ecg_p_04
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing77
Missing (%)6.5%
Memory size111.0 KiB
0.0
1111 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3339
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1111
93.4%
1.0 2
 
0.2%
(Missing) 77
 
6.5%

Length

2024-05-17T16:16:23.917456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:24.063218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1111
99.8%
1.0 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 2224
66.6%
. 1113
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2226
66.7%
Other Punctuation 1113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2224
99.9%
1 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2224
66.6%
. 1113
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2224
66.6%
. 1113
33.3%
1 2
 
0.1%

n_p_ecg_p_05
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing77
Missing (%)6.5%
Memory size111.0 KiB
0.0
1112 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3339
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1112
93.4%
1.0 1
 
0.1%
(Missing) 77
 
6.5%

Length

2024-05-17T16:16:24.210807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:24.346417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1112
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2225
66.6%
. 1113
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2226
66.7%
Other Punctuation 1113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2225
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2225
66.6%
. 1113
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2225
66.6%
. 1113
33.3%
1 1
 
< 0.1%

n_p_ecg_p_06
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing77
Missing (%)6.5%
Memory size111.0 KiB
0.0
1105 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3339
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1105
92.9%
1.0 8
 
0.7%
(Missing) 77
 
6.5%

Length

2024-05-17T16:16:24.501570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:24.640528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1105
99.3%
1.0 8
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 2218
66.4%
. 1113
33.3%
1 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2226
66.7%
Other Punctuation 1113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2218
99.6%
1 8
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2218
66.4%
. 1113
33.3%
1 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2218
66.4%
. 1113
33.3%
1 8
 
0.2%

n_p_ecg_p_07
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing77
Missing (%)6.5%
Memory size111.0 KiB
0.0
1045 
1.0
 
68

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3339
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1045
87.8%
1.0 68
 
5.7%
(Missing) 77
 
6.5%

Length

2024-05-17T16:16:24.800294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:24.944227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1045
93.9%
1.0 68
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 2158
64.6%
. 1113
33.3%
1 68
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2226
66.7%
Other Punctuation 1113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2158
96.9%
1 68
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2158
64.6%
. 1113
33.3%
1 68
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2158
64.6%
. 1113
33.3%
1 68
 
2.0%

n_p_ecg_p_08
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing77
Missing (%)6.5%
Memory size111.0 KiB
0.0
1109 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3339
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1109
93.2%
1.0 4
 
0.3%
(Missing) 77
 
6.5%

Length

2024-05-17T16:16:25.103449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:25.245037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1109
99.6%
1.0 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 2222
66.5%
. 1113
33.3%
1 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2226
66.7%
Other Punctuation 1113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2222
99.8%
1 4
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2222
66.5%
. 1113
33.3%
1 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2222
66.5%
. 1113
33.3%
1 4
 
0.1%

n_p_ecg_p_09
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing77
Missing (%)6.5%
Memory size111.0 KiB
0.0
1105 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3339
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1105
92.9%
1.0 8
 
0.7%
(Missing) 77
 
6.5%

Length

2024-05-17T16:16:25.389535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:25.533537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1105
99.3%
1.0 8
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 2218
66.4%
. 1113
33.3%
1 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2226
66.7%
Other Punctuation 1113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2218
99.6%
1 8
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2218
66.4%
. 1113
33.3%
1 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2218
66.4%
. 1113
33.3%
1 8
 
0.2%

n_p_ecg_p_10
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing77
Missing (%)6.5%
Memory size111.0 KiB
0.0
1095 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3339
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1095
92.0%
1.0 18
 
1.5%
(Missing) 77
 
6.5%

Length

2024-05-17T16:16:25.681537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:25.836155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1095
98.4%
1.0 18
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 2208
66.1%
. 1113
33.3%
1 18
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2226
66.7%
Other Punctuation 1113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2208
99.2%
1 18
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2208
66.1%
. 1113
33.3%
1 18
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2208
66.1%
. 1113
33.3%
1 18
 
0.5%

n_p_ecg_p_11
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing77
Missing (%)6.5%
Memory size111.0 KiB
0.0
1091 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3339
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1091
91.7%
1.0 22
 
1.8%
(Missing) 77
 
6.5%

Length

2024-05-17T16:16:25.988219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:26.164825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1091
98.0%
1.0 22
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 2204
66.0%
. 1113
33.3%
1 22
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2226
66.7%
Other Punctuation 1113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2204
99.0%
1 22
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2204
66.0%
. 1113
33.3%
1 22
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2204
66.0%
. 1113
33.3%
1 22
 
0.7%

n_p_ecg_p_12
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing77
Missing (%)6.5%
Memory size111.0 KiB
0.0
1074 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3339
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1074
90.3%
1.0 39
 
3.3%
(Missing) 77
 
6.5%

Length

2024-05-17T16:16:26.339151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:26.475745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1074
96.5%
1.0 39
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 2187
65.5%
. 1113
33.3%
1 39
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2226
66.7%
Other Punctuation 1113
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2187
98.2%
1 39
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 1113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3339
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2187
65.5%
. 1113
33.3%
1 39
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3339
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2187
65.5%
. 1113
33.3%
1 39
 
1.2%

fibr_ter_01
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing7
Missing (%)0.6%
Memory size111.3 KiB
0.0
1173 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3549
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1173
98.6%
1.0 10
 
0.8%
(Missing) 7
 
0.6%

Length

2024-05-17T16:16:26.629584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:26.775906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1173
99.2%
1.0 10
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 2356
66.4%
. 1183
33.3%
1 10
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2366
66.7%
Other Punctuation 1183
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2356
99.6%
1 10
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 1183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2356
66.4%
. 1183
33.3%
1 10
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2356
66.4%
. 1183
33.3%
1 10
 
0.3%

fibr_ter_02
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing7
Missing (%)0.6%
Memory size111.3 KiB
0.0
1177 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3549
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1177
98.9%
1.0 6
 
0.5%
(Missing) 7
 
0.6%

Length

2024-05-17T16:16:26.930856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:27.070384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1177
99.5%
1.0 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 2360
66.5%
. 1183
33.3%
1 6
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2366
66.7%
Other Punctuation 1183
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2360
99.7%
1 6
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 1183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2360
66.5%
. 1183
33.3%
1 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2360
66.5%
. 1183
33.3%
1 6
 
0.2%

fibr_ter_03
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing7
Missing (%)0.6%
Memory size111.3 KiB
0.0
1141 
1.0
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3549
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1141
95.9%
1.0 42
 
3.5%
(Missing) 7
 
0.6%

Length

2024-05-17T16:16:27.222681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:27.365339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1141
96.4%
1.0 42
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 2324
65.5%
. 1183
33.3%
1 42
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2366
66.7%
Other Punctuation 1183
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2324
98.2%
1 42
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 1183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2324
65.5%
. 1183
33.3%
1 42
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2324
65.5%
. 1183
33.3%
1 42
 
1.2%

fibr_ter_05
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing7
Missing (%)0.6%
Memory size111.3 KiB
0.0
1180 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3549
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1180
99.2%
1.0 3
 
0.3%
(Missing) 7
 
0.6%

Length

2024-05-17T16:16:27.522570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:27.662439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1180
99.7%
1.0 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 2363
66.6%
. 1183
33.3%
1 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2366
66.7%
Other Punctuation 1183
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2363
99.9%
1 3
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2363
66.6%
. 1183
33.3%
1 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2363
66.6%
. 1183
33.3%
1 3
 
0.1%

fibr_ter_06
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing7
Missing (%)0.6%
Memory size111.3 KiB
0.0
1177 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3549
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1177
98.9%
1.0 6
 
0.5%
(Missing) 7
 
0.6%

Length

2024-05-17T16:16:27.810823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:27.957550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1177
99.5%
1.0 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 2360
66.5%
. 1183
33.3%
1 6
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2366
66.7%
Other Punctuation 1183
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2360
99.7%
1 6
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 1183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2360
66.5%
. 1183
33.3%
1 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2360
66.5%
. 1183
33.3%
1 6
 
0.2%

fibr_ter_07
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing7
Missing (%)0.6%
Memory size111.3 KiB
0.0
1178 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3549
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1178
99.0%
1.0 5
 
0.4%
(Missing) 7
 
0.6%

Length

2024-05-17T16:16:28.123611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:28.271253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1178
99.6%
1.0 5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 2361
66.5%
. 1183
33.3%
1 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2366
66.7%
Other Punctuation 1183
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2361
99.8%
1 5
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 1183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2361
66.5%
. 1183
33.3%
1 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2361
66.5%
. 1183
33.3%
1 5
 
0.1%

fibr_ter_08
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing7
Missing (%)0.6%
Memory size111.3 KiB
0.0
1182 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3549
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1182
99.3%
1.0 1
 
0.1%
(Missing) 7
 
0.6%

Length

2024-05-17T16:16:28.420108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:28.567773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1182
99.9%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2365
66.6%
. 1183
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2366
66.7%
Other Punctuation 1183
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2365
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2365
66.6%
. 1183
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2365
66.6%
. 1183
33.3%
1 1
 
< 0.1%

GIPO_K
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing228
Missing (%)19.2%
Memory size110.4 KiB
0.0
596 
1.0
366 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2886
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 596
50.1%
1.0 366
30.8%
(Missing) 228
 
19.2%

Length

2024-05-17T16:16:28.752907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:28.889029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 596
62.0%
1.0 366
38.0%

Most occurring characters

ValueCountFrequency (%)
0 1558
54.0%
. 962
33.3%
1 366
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1924
66.7%
Other Punctuation 962
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1558
81.0%
1 366
 
19.0%
Other Punctuation
ValueCountFrequency (%)
. 962
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2886
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1558
54.0%
. 962
33.3%
1 366
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1558
54.0%
. 962
33.3%
1 366
 
12.7%

K_BLOOD
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)5.0%
Missing229
Missing (%)19.2%
Infinite0
Infinite (%)0.0%
Mean4.2149844
Minimum2.3
Maximum8.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:29.058014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile3.2
Q13.8
median4.1
Q34.6
95-th percentile5.5
Maximum8.2
Range5.9
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.74388075
Coefficient of variation (CV)0.17648482
Kurtosis2.886522
Mean4.2149844
Median Absolute Deviation (MAD)0.5
Skewness1.0061811
Sum4050.6
Variance0.55335857
MonotonicityNot monotonic
2024-05-17T16:16:29.267816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
4 77
 
6.5%
3.8 71
 
6.0%
4.2 61
 
5.1%
3.9 57
 
4.8%
4.5 48
 
4.0%
4.7 47
 
3.9%
4.3 47
 
3.9%
4.1 46
 
3.9%
4.6 44
 
3.7%
3.5 43
 
3.6%
Other values (38) 420
35.3%
(Missing) 229
19.2%
ValueCountFrequency (%)
2.3 1
 
0.1%
2.4 3
 
0.3%
2.5 1
 
0.1%
2.7 3
 
0.3%
2.8 3
 
0.3%
2.9 5
 
0.4%
3 15
1.3%
3.1 16
1.3%
3.2 21
1.8%
3.3 22
1.8%
ValueCountFrequency (%)
8.2 1
0.1%
8 1
0.1%
7.8 1
0.1%
7.7 1
0.1%
7.2 1
0.1%
6.9 2
0.2%
6.8 2
0.2%
6.7 2
0.2%
6.4 2
0.2%
6.2 1
0.1%

GIPER_NA
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing232
Missing (%)19.5%
Memory size110.4 KiB
0.0
934 
1.0
 
24

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2874
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 934
78.5%
1.0 24
 
2.0%
(Missing) 232
 
19.5%

Length

2024-05-17T16:16:29.466729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:29.599416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 934
97.5%
1.0 24
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 1892
65.8%
. 958
33.3%
1 24
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1916
66.7%
Other Punctuation 958
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1892
98.7%
1 24
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 958
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2874
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1892
65.8%
. 958
33.3%
1 24
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1892
65.8%
. 958
33.3%
1 24
 
0.8%

NA_BLOOD
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)4.0%
Missing232
Missing (%)19.5%
Infinite0
Infinite (%)0.0%
Mean136.83612
Minimum117
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:29.785067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum117
5-th percentile125.85
Q1133
median137
Q3140
95-th percentile146
Maximum163
Range46
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.4420307
Coefficient of variation (CV)0.047078439
Kurtosis0.97595307
Mean136.83612
Median Absolute Deviation (MAD)3
Skewness0.043731444
Sum131089
Variance41.49976
MonotonicityNot monotonic
2024-05-17T16:16:30.029459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
136 142
11.9%
140 142
11.9%
130 72
 
6.1%
138 66
 
5.5%
132 52
 
4.4%
134 47
 
3.9%
133 42
 
3.5%
143 42
 
3.5%
146 41
 
3.4%
139 40
 
3.4%
Other values (28) 272
22.9%
(Missing) 232
19.5%
ValueCountFrequency (%)
117 4
 
0.3%
120 6
 
0.5%
121 4
 
0.3%
122 4
 
0.3%
123 12
1.0%
124 8
0.7%
125 10
0.8%
126 8
0.7%
127 11
0.9%
128 16
1.3%
ValueCountFrequency (%)
163 1
 
0.1%
159 3
 
0.3%
156 3
 
0.3%
154 2
 
0.2%
153 11
0.9%
151 1
 
0.1%
150 15
1.3%
149 2
 
0.2%
148 2
 
0.2%
147 1
 
0.1%

ALT_BLOOD
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct54
Distinct (%)5.3%
Missing174
Missing (%)14.6%
Infinite0
Infinite (%)0.0%
Mean0.49041339
Minimum0.03
Maximum2.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:30.230923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.15
Q10.23
median0.38
Q30.61
95-th percentile1.36
Maximum2.86
Range2.83
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.39116225
Coefficient of variation (CV)0.79761741
Kurtosis5.9664197
Mean0.49041339
Median Absolute Deviation (MAD)0.15
Skewness2.1427277
Sum498.26
Variance0.15300791
MonotonicityNot monotonic
2024-05-17T16:16:30.469665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15 170
14.3%
0.3 146
12.3%
0.45 123
10.3%
0.23 111
9.3%
0.38 96
8.1%
0.61 55
 
4.6%
0.75 52
 
4.4%
0.52 45
 
3.8%
0.9 26
 
2.2%
0.68 21
 
1.8%
Other values (44) 171
14.4%
(Missing) 174
14.6%
ValueCountFrequency (%)
0.03 1
 
0.1%
0.07 12
 
1.0%
0.14 4
 
0.3%
0.15 170
14.3%
0.18 2
 
0.2%
0.2 1
 
0.1%
0.22 4
 
0.3%
0.23 111
9.3%
0.26 2
 
0.2%
0.3 146
12.3%
ValueCountFrequency (%)
2.86 1
 
0.1%
2.72 1
 
0.1%
2.56 1
 
0.1%
2.26 3
0.3%
2.12 1
 
0.1%
2.1 4
0.3%
1.96 3
0.3%
1.89 1
 
0.1%
1.8 2
0.2%
1.74 1
 
0.1%

AST_BLOOD
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)4.9%
Missing175
Missing (%)14.7%
Infinite0
Infinite (%)0.0%
Mean0.26061084
Minimum0.04
Maximum2.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:31.302321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.07
Q10.15
median0.22
Q30.33
95-th percentile0.6
Maximum2.15
Range2.11
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.19781404
Coefficient of variation (CV)0.75903997
Kurtosis12.819355
Mean0.26061084
Median Absolute Deviation (MAD)0.08
Skewness2.6450289
Sum264.52
Variance0.039130396
MonotonicityNot monotonic
2024-05-17T16:16:31.510091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15 193
16.2%
0.22 121
10.2%
0.07 114
9.6%
0.3 109
9.2%
0.11 82
6.9%
0.18 81
6.8%
0.37 63
 
5.3%
0.45 50
 
4.2%
0.26 34
 
2.9%
0.52 23
 
1.9%
Other values (40) 145
12.2%
(Missing) 175
14.7%
ValueCountFrequency (%)
0.04 10
 
0.8%
0.07 114
9.6%
0.08 2
 
0.2%
0.1 2
 
0.2%
0.11 82
6.9%
0.12 1
 
0.1%
0.13 1
 
0.1%
0.14 2
 
0.2%
0.15 193
16.2%
0.18 81
6.8%
ValueCountFrequency (%)
2.15 1
 
0.1%
1.34 3
0.3%
1.2 1
 
0.1%
1.13 1
 
0.1%
1.12 2
0.2%
1.05 1
 
0.1%
1.04 3
0.3%
0.96 3
0.3%
0.9 4
0.3%
0.86 1
 
0.1%

KFK_BLOOD
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing1189
Missing (%)99.9%
Memory size106.7 KiB
1.8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.8

Common Values

ValueCountFrequency (%)
1.8 1
 
0.1%
(Missing) 1189
99.9%

Length

2024-05-17T16:16:31.699029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:31.830035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.8 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
8 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2
66.7%
Other Punctuation 1
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1
50.0%
8 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
8 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
8 1
33.3%

L_BLOOD
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct148
Distinct (%)13.2%
Missing71
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean8.522252
Minimum2
Maximum27.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:31.990684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.59
Q16.3
median7.9
Q310.1
95-th percentile14.61
Maximum27.9
Range25.9
Interquartile range (IQR)3.8

Descriptive statistics

Standard deviation3.2222999
Coefficient of variation (CV)0.37810427
Kurtosis3.1767031
Mean8.522252
Median Absolute Deviation (MAD)1.9
Skewness1.3588503
Sum9536.4
Variance10.383216
MonotonicityNot monotonic
2024-05-17T16:16:32.204721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 24
 
2.0%
8 23
 
1.9%
7.2 23
 
1.9%
5 22
 
1.8%
6 22
 
1.8%
7 21
 
1.8%
7.3 21
 
1.8%
6.4 21
 
1.8%
6.9 20
 
1.7%
7.4 20
 
1.7%
Other values (138) 902
75.8%
(Missing) 71
 
6.0%
ValueCountFrequency (%)
2 1
 
0.1%
2.1 1
 
0.1%
3.2 2
 
0.2%
3.4 1
 
0.1%
3.5 1
 
0.1%
3.6 1
 
0.1%
3.7 1
 
0.1%
3.8 2
 
0.2%
3.9 2
 
0.2%
4 9
0.8%
ValueCountFrequency (%)
27.9 1
0.1%
25 1
0.1%
24.9 1
0.1%
23.5 1
0.1%
22.9 1
0.1%
21.3 1
0.1%
20.1 1
0.1%
19.4 1
0.1%
18.6 2
0.2%
18.4 1
0.1%

ROE
Real number (ℝ)

MISSING 

Distinct56
Distinct (%)5.2%
Missing114
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean12.768587
Minimum1
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:32.404721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median10
Q317
95-th percentile34
Maximum140
Range139
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.923982
Coefficient of variation (CV)0.85553565
Kurtosis19.205505
Mean12.768587
Median Absolute Deviation (MAD)5
Skewness2.8319741
Sum13739
Variance119.33338
MonotonicityNot monotonic
2024-05-17T16:16:32.638269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 96
 
8.1%
5 93
 
7.8%
4 81
 
6.8%
10 77
 
6.5%
7 68
 
5.7%
8 58
 
4.9%
6 57
 
4.8%
15 50
 
4.2%
12 44
 
3.7%
20 42
 
3.5%
Other values (46) 410
34.5%
(Missing) 114
 
9.6%
ValueCountFrequency (%)
1 1
 
0.1%
2 33
 
2.8%
3 96
8.1%
4 81
6.8%
5 93
7.8%
6 57
4.8%
7 68
5.7%
8 58
4.9%
9 40
3.4%
10 77
6.5%
ValueCountFrequency (%)
140 1
0.1%
68 1
0.1%
65 1
0.1%
61 1
0.1%
59 1
0.1%
57 2
0.2%
55 1
0.1%
53 1
0.1%
51 1
0.1%
50 2
0.2%

TIME_B_S
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)0.8%
Missing89
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean4.9609446
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-05-17T16:16:32.814255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.9315402
Coefficient of variation (CV)0.59092379
Kurtosis-1.5419468
Mean4.9609446
Median Absolute Deviation (MAD)3
Skewness0.1137526
Sum5462
Variance8.5939278
MonotonicityNot monotonic
2024-05-17T16:16:32.980903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 237
19.9%
9 223
18.7%
1 124
10.4%
7 117
9.8%
3 116
9.7%
6 98
8.2%
8 76
 
6.4%
4 56
 
4.7%
5 54
 
4.5%
(Missing) 89
 
7.5%
ValueCountFrequency (%)
1 124
10.4%
2 237
19.9%
3 116
9.7%
4 56
 
4.7%
5 54
 
4.5%
6 98
8.2%
7 117
9.8%
8 76
 
6.4%
9 223
18.7%
ValueCountFrequency (%)
9 223
18.7%
8 76
 
6.4%
7 117
9.8%
6 98
8.2%
5 54
 
4.5%
4 56
 
4.7%
3 116
9.7%
2 237
19.9%
1 124
10.4%

R_AB_1_n
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing3
Missing (%)0.3%
Memory size111.3 KiB
0.0
941 
1.0
190 
2.0
 
47
3.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3561
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row3.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 941
79.1%
1.0 190
 
16.0%
2.0 47
 
3.9%
3.0 9
 
0.8%
(Missing) 3
 
0.3%

Length

2024-05-17T16:16:33.167717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:33.324553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 941
79.3%
1.0 190
 
16.0%
2.0 47
 
4.0%
3.0 9
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 2128
59.8%
. 1187
33.3%
1 190
 
5.3%
2 47
 
1.3%
3 9
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2374
66.7%
Other Punctuation 1187
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2128
89.6%
1 190
 
8.0%
2 47
 
2.0%
3 9
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 1187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3561
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2128
59.8%
. 1187
33.3%
1 190
 
5.3%
2 47
 
1.3%
3 9
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3561
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2128
59.8%
. 1187
33.3%
1 190
 
5.3%
2 47
 
1.3%
3 9
 
0.3%

R_AB_2_n
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing3
Missing (%)0.3%
Memory size111.3 KiB
0.0
1077 
1.0
 
85
2.0
 
25

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3561
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1077
90.5%
1.0 85
 
7.1%
2.0 25
 
2.1%
(Missing) 3
 
0.3%

Length

2024-05-17T16:16:33.498365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:33.642053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1077
90.7%
1.0 85
 
7.2%
2.0 25
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 2264
63.6%
. 1187
33.3%
1 85
 
2.4%
2 25
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2374
66.7%
Other Punctuation 1187
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2264
95.4%
1 85
 
3.6%
2 25
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 1187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3561
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2264
63.6%
. 1187
33.3%
1 85
 
2.4%
2 25
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3561
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2264
63.6%
. 1187
33.3%
1 85
 
2.4%
2 25
 
0.7%

R_AB_3_n
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing4
Missing (%)0.3%
Memory size111.3 KiB
0.0
1131 
1.0
 
50
2.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3558
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1131
95.0%
1.0 50
 
4.2%
2.0 5
 
0.4%
(Missing) 4
 
0.3%

Length

2024-05-17T16:16:33.835804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:33.981191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1131
95.4%
1.0 50
 
4.2%
2.0 5
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 2317
65.1%
. 1186
33.3%
1 50
 
1.4%
2 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2372
66.7%
Other Punctuation 1186
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2317
97.7%
1 50
 
2.1%
2 5
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 1186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3558
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2317
65.1%
. 1186
33.3%
1 50
 
1.4%
2 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3558
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2317
65.1%
. 1186
33.3%
1 50
 
1.4%
2 5
 
0.1%

NA_KB
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.3%
Missing436
Missing (%)36.6%
Memory size109.6 KiB
1.0
435 
0.0
319 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2262
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 435
36.6%
0.0 319
26.8%
(Missing) 436
36.6%

Length

2024-05-17T16:16:34.165878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:34.321760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 435
57.7%
0.0 319
42.3%

Most occurring characters

ValueCountFrequency (%)
0 1073
47.4%
. 754
33.3%
1 435
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1508
66.7%
Other Punctuation 754
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1073
71.2%
1 435
28.8%
Other Punctuation
ValueCountFrequency (%)
. 754
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2262
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1073
47.4%
. 754
33.3%
1 435
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1073
47.4%
. 754
33.3%
1 435
19.2%

NOT_NA_KB
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.3%
Missing448
Missing (%)37.6%
Memory size109.6 KiB
1.0
523 
0.0
219 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2226
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 523
43.9%
0.0 219
18.4%
(Missing) 448
37.6%

Length

2024-05-17T16:16:34.493713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:34.653328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 523
70.5%
0.0 219
29.5%

Most occurring characters

ValueCountFrequency (%)
0 961
43.2%
. 742
33.3%
1 523
23.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1484
66.7%
Other Punctuation 742
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 961
64.8%
1 523
35.2%
Other Punctuation
ValueCountFrequency (%)
. 742
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2226
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 961
43.2%
. 742
33.3%
1 523
23.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 961
43.2%
. 742
33.3%
1 523
23.5%

LID_KB
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.3%
Missing438
Missing (%)36.8%
Memory size109.6 KiB
0.0
467 
1.0
285 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2256
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 467
39.2%
1.0 285
23.9%
(Missing) 438
36.8%

Length

2024-05-17T16:16:34.818328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:34.972735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 467
62.1%
1.0 285
37.9%

Most occurring characters

ValueCountFrequency (%)
0 1219
54.0%
. 752
33.3%
1 285
 
12.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1504
66.7%
Other Punctuation 752
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1219
81.1%
1 285
 
18.9%
Other Punctuation
ValueCountFrequency (%)
. 752
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1219
54.0%
. 752
33.3%
1 285
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1219
54.0%
. 752
33.3%
1 285
 
12.6%

NITR_S
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing7
Missing (%)0.6%
Memory size111.3 KiB
0.0
1105 
1.0
 
78

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3549
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1105
92.9%
1.0 78
 
6.6%
(Missing) 7
 
0.6%

Length

2024-05-17T16:16:35.134736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:35.278635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1105
93.4%
1.0 78
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 2288
64.5%
. 1183
33.3%
1 78
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2366
66.7%
Other Punctuation 1183
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2288
96.7%
1 78
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 1183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2288
64.5%
. 1183
33.3%
1 78
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2288
64.5%
. 1183
33.3%
1 78
 
2.2%

NA_R_1_n
Categorical

Distinct5
Distinct (%)0.4%
Missing3
Missing (%)0.3%
Memory size111.3 KiB
0.0
836 
1.0
259 
2.0
 
72
3.0
 
16
4.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3561
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 836
70.3%
1.0 259
 
21.8%
2.0 72
 
6.1%
3.0 16
 
1.3%
4.0 4
 
0.3%
(Missing) 3
 
0.3%

Length

2024-05-17T16:16:35.449306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:35.617115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 836
70.4%
1.0 259
 
21.8%
2.0 72
 
6.1%
3.0 16
 
1.3%
4.0 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 2023
56.8%
. 1187
33.3%
1 259
 
7.3%
2 72
 
2.0%
3 16
 
0.4%
4 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2374
66.7%
Other Punctuation 1187
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2023
85.2%
1 259
 
10.9%
2 72
 
3.0%
3 16
 
0.7%
4 4
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 1187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3561
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2023
56.8%
. 1187
33.3%
1 259
 
7.3%
2 72
 
2.0%
3 16
 
0.4%
4 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3561
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2023
56.8%
. 1187
33.3%
1 259
 
7.3%
2 72
 
2.0%
3 16
 
0.4%
4 4
 
0.1%

NA_R_2_n
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing3
Missing (%)0.3%
Memory size111.3 KiB
0.0
1115 
1.0
 
57
2.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3561
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1115
93.7%
1.0 57
 
4.8%
2.0 15
 
1.3%
(Missing) 3
 
0.3%

Length

2024-05-17T16:16:35.799551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:35.964417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1115
93.9%
1.0 57
 
4.8%
2.0 15
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 2302
64.6%
. 1187
33.3%
1 57
 
1.6%
2 15
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2374
66.7%
Other Punctuation 1187
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2302
97.0%
1 57
 
2.4%
2 15
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 1187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3561
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2302
64.6%
. 1187
33.3%
1 57
 
1.6%
2 15
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3561
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2302
64.6%
. 1187
33.3%
1 57
 
1.6%
2 15
 
0.4%

NA_R_3_n
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing5
Missing (%)0.4%
Memory size111.3 KiB
0.0
1148 
1.0
 
30
2.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3555
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1148
96.5%
1.0 30
 
2.5%
2.0 7
 
0.6%
(Missing) 5
 
0.4%

Length

2024-05-17T16:16:36.183745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:36.326584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1148
96.9%
1.0 30
 
2.5%
2.0 7
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 2333
65.6%
. 1185
33.3%
1 30
 
0.8%
2 7
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2370
66.7%
Other Punctuation 1185
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2333
98.4%
1 30
 
1.3%
2 7
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 1185
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3555
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2333
65.6%
. 1185
33.3%
1 30
 
0.8%
2 7
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3555
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2333
65.6%
. 1185
33.3%
1 30
 
0.8%
2 7
 
0.2%

NOT_NA_1_n
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.4%
Missing5
Missing (%)0.4%
Memory size111.3 KiB
0.0
860 
1.0
269 
2.0
 
39
3.0
 
11
4.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3555
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row3.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 860
72.3%
1.0 269
 
22.6%
2.0 39
 
3.3%
3.0 11
 
0.9%
4.0 6
 
0.5%
(Missing) 5
 
0.4%

Length

2024-05-17T16:16:36.482849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:36.636315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 860
72.6%
1.0 269
 
22.7%
2.0 39
 
3.3%
3.0 11
 
0.9%
4.0 6
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 2045
57.5%
. 1185
33.3%
1 269
 
7.6%
2 39
 
1.1%
3 11
 
0.3%
4 6
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2370
66.7%
Other Punctuation 1185
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2045
86.3%
1 269
 
11.4%
2 39
 
1.6%
3 11
 
0.5%
4 6
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 1185
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3555
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2045
57.5%
. 1185
33.3%
1 269
 
7.6%
2 39
 
1.1%
3 11
 
0.3%
4 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3555
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2045
57.5%
. 1185
33.3%
1 269
 
7.6%
2 39
 
1.1%
3 11
 
0.3%
4 6
 
0.2%

NOT_NA_2_n
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing5
Missing (%)0.4%
Memory size111.3 KiB
0.0
1093 
1.0
 
64
2.0
 
25
3.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3555
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row2.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1093
91.8%
1.0 64
 
5.4%
2.0 25
 
2.1%
3.0 3
 
0.3%
(Missing) 5
 
0.4%

Length

2024-05-17T16:16:36.808352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:36.959189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1093
92.2%
1.0 64
 
5.4%
2.0 25
 
2.1%
3.0 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 2278
64.1%
. 1185
33.3%
1 64
 
1.8%
2 25
 
0.7%
3 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2370
66.7%
Other Punctuation 1185
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2278
96.1%
1 64
 
2.7%
2 25
 
1.1%
3 3
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 1185
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3555
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2278
64.1%
. 1185
33.3%
1 64
 
1.8%
2 25
 
0.7%
3 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3555
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2278
64.1%
. 1185
33.3%
1 64
 
1.8%
2 25
 
0.7%
3 3
 
0.1%

NOT_NA_3_n
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing6
Missing (%)0.5%
Memory size111.3 KiB
0.0
1126 
1.0
 
31
2.0
 
27

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3552
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row2.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1126
94.6%
1.0 31
 
2.6%
2.0 27
 
2.3%
(Missing) 6
 
0.5%

Length

2024-05-17T16:16:37.128402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:37.268570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1126
95.1%
1.0 31
 
2.6%
2.0 27
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 2310
65.0%
. 1184
33.3%
1 31
 
0.9%
2 27
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2368
66.7%
Other Punctuation 1184
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2310
97.6%
1 31
 
1.3%
2 27
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 1184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2310
65.0%
. 1184
33.3%
1 31
 
0.9%
2 27
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2310
65.0%
. 1184
33.3%
1 31
 
0.9%
2 27
 
0.8%

LID_S_n
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing7
Missing (%)0.6%
Memory size111.3 KiB
0.0
849 
1.0
334 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3549
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 849
71.3%
1.0 334
 
28.1%
(Missing) 7
 
0.6%

Length

2024-05-17T16:16:37.426079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:37.565772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 849
71.8%
1.0 334
 
28.2%

Most occurring characters

ValueCountFrequency (%)
0 2032
57.3%
. 1183
33.3%
1 334
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2366
66.7%
Other Punctuation 1183
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2032
85.9%
1 334
 
14.1%
Other Punctuation
ValueCountFrequency (%)
. 1183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2032
57.3%
. 1183
33.3%
1 334
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2032
57.3%
. 1183
33.3%
1 334
 
9.4%

B_BLOK_S_n
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing7
Missing (%)0.6%
Memory size111.3 KiB
0.0
1016 
1.0
167 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3549
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1016
85.4%
1.0 167
 
14.0%
(Missing) 7
 
0.6%

Length

2024-05-17T16:16:37.717341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:37.863094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1016
85.9%
1.0 167
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 2199
62.0%
. 1183
33.3%
1 167
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2366
66.7%
Other Punctuation 1183
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2199
92.9%
1 167
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 1183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2199
62.0%
. 1183
33.3%
1 167
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2199
62.0%
. 1183
33.3%
1 167
 
4.7%

ANT_CA_S_n
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing9
Missing (%)0.8%
Memory size111.3 KiB
1.0
842 
0.0
339 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3543
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 842
70.8%
0.0 339
28.5%
(Missing) 9
 
0.8%

Length

2024-05-17T16:16:38.031593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:38.172602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 842
71.3%
0.0 339
28.7%

Most occurring characters

ValueCountFrequency (%)
0 1520
42.9%
. 1181
33.3%
1 842
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2362
66.7%
Other Punctuation 1181
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1520
64.4%
1 842
35.6%
Other Punctuation
ValueCountFrequency (%)
. 1181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3543
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1520
42.9%
. 1181
33.3%
1 842
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3543
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1520
42.9%
. 1181
33.3%
1 842
23.8%

GEPAR_S_n
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing13
Missing (%)1.1%
Memory size111.3 KiB
1.0
836 
0.0
341 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3531
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 836
70.3%
0.0 341
28.7%
(Missing) 13
 
1.1%

Length

2024-05-17T16:16:38.333769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:38.488425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 836
71.0%
0.0 341
29.0%

Most occurring characters

ValueCountFrequency (%)
0 1518
43.0%
. 1177
33.3%
1 836
23.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2354
66.7%
Other Punctuation 1177
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1518
64.5%
1 836
35.5%
Other Punctuation
ValueCountFrequency (%)
. 1177
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3531
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1518
43.0%
. 1177
33.3%
1 836
23.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3531
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1518
43.0%
. 1177
33.3%
1 836
23.7%

ASP_S_n
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing13
Missing (%)1.1%
Memory size111.3 KiB
1.0
910 
0.0
267 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3531
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 910
76.5%
0.0 267
 
22.4%
(Missing) 13
 
1.1%

Length

2024-05-17T16:16:38.653544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:38.796407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 910
77.3%
0.0 267
 
22.7%

Most occurring characters

ValueCountFrequency (%)
0 1444
40.9%
. 1177
33.3%
1 910
25.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2354
66.7%
Other Punctuation 1177
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1444
61.3%
1 910
38.7%
Other Punctuation
ValueCountFrequency (%)
. 1177
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3531
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1444
40.9%
. 1177
33.3%
1 910
25.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3531
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1444
40.9%
. 1177
33.3%
1 910
25.8%

TIKL_S_n
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing11
Missing (%)0.9%
Memory size111.3 KiB
0.0
1154 
1.0
 
25

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1154
97.0%
1.0 25
 
2.1%
(Missing) 11
 
0.9%

Length

2024-05-17T16:16:38.960448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:39.111921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1154
97.9%
1.0 25
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 2333
66.0%
. 1179
33.3%
1 25
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2358
66.7%
Other Punctuation 1179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2333
98.9%
1 25
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 1179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2333
66.0%
. 1179
33.3%
1 25
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2333
66.0%
. 1179
33.3%
1 25
 
0.7%

TRENT_S_n
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing11
Missing (%)0.9%
Memory size111.3 KiB
0.0
921 
1.0
258 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 921
77.4%
1.0 258
 
21.7%
(Missing) 11
 
0.9%

Length

2024-05-17T16:16:39.269006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-17T16:16:39.418806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 921
78.1%
1.0 258
 
21.9%

Most occurring characters

ValueCountFrequency (%)
0 2100
59.4%
. 1179
33.3%
1 258
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2358
66.7%
Other Punctuation 1179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2100
89.1%
1 258
 
10.9%
Other Punctuation
ValueCountFrequency (%)
. 1179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2100
59.4%
. 1179
33.3%
1 258
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2100
59.4%
. 1179
33.3%
1 258
 
7.3%

Interactions

2024-05-17T16:15:57.258245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:22.906128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:27.602425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:30.072511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:32.496488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:34.704738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:37.048153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:39.595963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:42.369878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:44.831811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:47.102544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:50.185027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:52.438143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:54.688936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:57.443950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:24.639257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:27.788095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:30.246818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:32.669596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:34.885333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:37.215434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:39.761539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:42.599606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:44.995022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:47.266277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:50.329397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:52.592398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:54.851754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:57.611075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:24.818408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:27.934210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:30.399162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:32.814177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:35.049131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:37.454669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:39.958825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:42.811437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:45.161362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:47.435429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:50.494526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:52.744055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:55.029793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:57.806441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:24.976421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:28.095403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:30.572543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:32.963564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:35.235511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:37.640007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:40.138016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:43.010792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:45.331475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:47.607265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:50.676598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:52.921459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:55.249297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:57.963486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:25.133288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:28.231802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:30.726959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:33.099737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:35.385920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:37.781218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:40.274693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:43.180325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:45.481308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:47.789764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:50.841622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:53.078303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:55.419001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:58.116987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:25.314779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:28.407804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:30.885435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:33.254507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:35.543091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:37.932095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:40.462124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:43.333833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:45.629286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:47.993857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:51.022837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:53.234905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:55.606426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:58.293302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:25.492395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:28.603228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:31.081630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:33.433661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:35.731072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:38.112306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:40.656324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:43.497769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:45.791263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:48.184389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:51.185062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:53.399661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:55.777864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:58.506451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:25.699827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:28.815344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:31.285157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:33.557343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:35.870848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:38.322652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:40.870315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:43.664656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:45.957972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:48.351911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:51.345801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:53.568612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:55.969882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:58.668176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:25.877052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:29.000757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:31.440544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:33.704584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:36.029904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:38.489019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:41.053585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:43.827120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:46.113206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:48.520059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:51.498855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:53.727405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:56.147571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:58.835134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:26.032482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:29.167022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:31.607871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:33.846247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:36.189201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:38.632449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:41.244849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:44.012509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:46.258041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:48.680559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:51.644283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:53.885404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:56.321505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:59.025644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:26.967194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:29.360939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:31.787042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:33.997298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:36.360995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:38.815545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:41.431719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:44.183619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:46.439271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:48.836972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:51.793162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:54.072856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:56.543670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:59.206876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:27.123749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:29.523887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:31.963572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:34.203519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:36.521990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:39.010166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:41.634076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:44.330373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:46.597955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:49.004731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:51.937424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:54.230086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:56.701997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:59.398962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:27.278730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:29.699855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:32.135093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:34.348212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:36.692350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:39.194919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:41.835510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:44.484818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:46.743113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:49.161982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:52.093961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:54.370476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:56.862248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:59.570407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:27.431829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:29.882396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:32.313025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:34.543957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:36.880136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:39.383000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:42.073622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:44.652635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:46.924679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:49.994947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:52.259892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:54.522282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-17T16:15:57.070002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-17T16:16:39.794958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AGEALT_BLOODANT_CA_S_nASP_S_nAST_BLOODB_BLOK_S_nDLIT_AGD_AD_KBRIGD_AD_ORITFIB_G_POSTFK_STENOKGBGEPAR_S_nGIPER_NAGIPO_KGT_POSTIBS_NASLIBS_POSTIM_PG_PINF_ANAMK_BLOODK_SH_POSTLID_KBLID_S_nL_BLOODMP_TP_POSTNA_BLOODNA_KBNA_R_1_nNA_R_2_nNA_R_3_nNITR_SNOT_NA_1_nNOT_NA_2_nNOT_NA_3_nNOT_NA_KBO_L_POSTROER_AB_1_nR_AB_2_nR_AB_3_nSEXSIM_GIPERTSTENOK_ANSVT_POSTS_AD_KBRIGS_AD_ORITTIKL_S_nTIME_B_STRENT_S_nZSN_Aant_imendocr_01endocr_02endocr_03fibr_ter_01fibr_ter_02fibr_ter_03fibr_ter_05fibr_ter_06fibr_ter_07fibr_ter_08inf_imlat_imn_p_ecg_p_01n_p_ecg_p_03n_p_ecg_p_04n_p_ecg_p_05n_p_ecg_p_06n_p_ecg_p_07n_p_ecg_p_08n_p_ecg_p_09n_p_ecg_p_10n_p_ecg_p_11n_p_ecg_p_12n_r_ecg_p_01n_r_ecg_p_02n_r_ecg_p_03n_r_ecg_p_04n_r_ecg_p_05n_r_ecg_p_06n_r_ecg_p_08n_r_ecg_p_09n_r_ecg_p_10np_01np_04np_05np_07np_08np_09np_10nr_01nr_02nr_03nr_04nr_07nr_08nr_11post_imritm_ecg_p_01ritm_ecg_p_02ritm_ecg_p_04ritm_ecg_p_06ritm_ecg_p_07ritm_ecg_p_08zab_leg_01zab_leg_02zab_leg_03zab_leg_04zab_leg_06
AGE1.000-0.1690.1070.000-0.1250.1560.2710.0300.0090.0460.1180.1050.1260.0590.0470.0840.0000.1430.0000.0650.0250.0000.0630.000-0.0270.0790.0360.0000.0360.0160.0000.0000.0170.0000.0000.0000.1100.2030.0000.0000.0000.3890.0950.2110.0000.1440.1230.0630.0290.0000.0580.0000.1660.0170.0650.0000.0400.1850.0000.0970.1670.0000.0000.0600.0000.0240.0000.0360.0110.0000.0490.1030.0810.0000.1150.1470.0000.0000.0000.0690.0780.0660.0000.0000.0000.0000.1090.0000.0000.0890.0000.0000.0000.0000.0560.0000.0000.0990.0000.0910.0730.0000.0000.0430.0000.0720.0460.0320.0140.000
ALT_BLOOD-0.1691.0000.0820.0450.5380.090-0.089-0.038-0.0360.0000.0000.0000.0580.0540.0000.0000.0000.0140.0000.0430.0200.0000.0000.084-0.0030.0770.0070.0000.0900.0000.0500.0280.0380.0210.0670.0760.000-0.0260.0000.0000.0000.0950.000-0.0510.000-0.007-0.0560.0000.0040.0000.2160.0000.0000.0000.0000.0000.0600.0000.1950.0190.0001.0000.0000.0000.0000.0420.0001.0000.0000.0530.0000.0000.0000.0300.0420.0000.0000.0000.1480.1310.0550.0000.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.0000.0820.0000.0000.0340.0000.0530.1890.0000.0000.000
ANT_CA_S_n0.1070.0821.0000.000-0.0940.1910.1070.1160.1100.0220.0000.0920.0720.0220.0000.0000.0000.0460.0130.000-0.0030.0000.0780.0320.0120.0000.0390.0520.0500.0000.0000.0000.0000.0280.0000.0000.0000.0490.0000.0380.0000.0410.0000.0200.0370.1130.1280.0000.0150.0000.0480.0620.0680.0000.0000.0690.0000.0250.0000.0000.0001.0000.0000.0640.0000.0590.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0260.0000.0000.0000.0230.0220.0220.0000.0000.000
ASP_S_n0.0000.0450.0001.0000.0040.0000.0300.0040.0620.0000.1180.0940.1920.0000.0000.0000.0000.0000.0000.000-0.0130.0000.0000.037-0.0690.023-0.0530.0300.0000.0540.1110.0000.0000.0560.0000.0500.0000.0040.0000.0680.0800.0000.000-0.0430.015-0.0020.0620.1210.0290.2430.0270.0000.0000.0000.0000.0000.0000.0570.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0440.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0260.0000.0150.0000.0000.0260.0000.0140.0180.0000.0160.000
AST_BLOOD-0.1250.538-0.0940.0041.0000.063-0.072-0.045-0.0430.0000.0000.0400.0260.0270.0220.0000.1870.0180.0000.0260.0280.0000.0000.0000.0500.0450.0120.0000.0000.0170.0570.0000.0560.0760.0830.0000.000-0.0410.0000.0240.0000.1120.000-0.0510.000-0.048-0.0580.0000.0500.0000.0770.0670.0000.0000.0000.0000.0000.0490.1110.0360.0001.0000.0000.0270.0000.0000.0001.0000.1160.0000.0000.0000.1140.0000.0000.0000.0000.0180.0600.0710.0000.0000.0000.1980.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0410.0470.0670.0000.0000.0000.0460.0000.0000.0000.0000.0000.000
B_BLOK_S_n0.1560.0900.1910.0000.0631.0000.0030.0110.1440.0000.0610.0130.0000.0000.0000.0000.1980.0520.0000.000-0.0010.0000.0000.000-0.0560.0160.0070.1080.0220.0000.0110.0000.0000.0280.0420.0540.033-0.0130.0230.0000.0320.0370.061-0.0610.0000.0230.1340.0000.0880.0180.0000.1340.0680.0000.0000.0770.0000.0000.0000.0000.0001.0000.1440.1320.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0340.0000.0300.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.1320.0290.0000.0000.0000.0000.0170.0330.0280.0000.0000.000
DLIT_AG0.271-0.0890.1070.030-0.0720.0031.0000.2980.3160.0290.0530.5560.0000.0000.0930.0000.0000.1340.0190.0360.0580.0000.0000.072-0.0190.0000.0650.0640.0570.0600.0000.0640.0720.1030.0450.0770.1060.1620.0160.0430.0000.3400.1450.1770.1490.3320.3880.0640.0460.0000.0000.0600.1770.0830.0490.0000.0000.0410.0200.0630.0000.0000.0000.0820.0000.0000.0680.0000.0000.0000.0000.0000.0390.0000.0000.0000.0000.0680.0570.0000.0000.1520.0000.0000.1110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0000.1300.0000.0000.0000.0920.0140.0000.0790.0000.0000.000
D_AD_KBRIG0.030-0.0380.1160.004-0.0450.0110.2981.0000.3800.0820.0000.1550.1010.0000.0000.3810.0000.0000.0510.0000.0060.6920.1040.000-0.0900.172-0.0670.0000.0000.0000.0000.1460.0000.0000.1720.1830.1360.0870.0000.0000.1230.1730.000-0.0360.0000.8380.3920.0000.0530.0000.2170.2100.0000.0000.0000.0000.0000.1721.0001.0000.0251.0000.0660.1790.0000.1550.0001.0000.0000.0000.0000.0000.3810.0000.0000.0990.0000.0000.2330.0000.2150.0001.0001.0000.0001.0000.2321.0000.0001.0001.0000.0000.0000.0000.2120.0000.3800.2670.0000.0380.1000.1591.0000.0920.0000.0000.1890.0000.3800.000
D_AD_ORIT0.009-0.0360.1100.062-0.0430.1440.3160.3801.0000.1070.0210.1640.0680.0000.0410.0640.0900.0000.0000.0000.0390.1830.0000.047-0.0980.0200.0590.0000.0420.0000.0730.0830.0000.0000.0220.1040.0000.0300.0000.0000.0000.0930.159-0.0080.0000.4410.8260.0340.0860.0000.0000.0330.0170.0540.0000.0000.1210.0000.0001.0000.0000.0000.0620.0470.0000.1600.0001.0000.1230.0000.0000.0000.0270.0000.0000.0790.0770.0200.0000.0800.0270.0000.0000.0001.0000.0000.1880.0000.0001.0001.0000.0000.0000.0160.0000.0000.0000.0620.0000.0720.0680.1231.0000.0840.0750.0000.0560.2780.0000.000
FIB_G_POST0.0460.0000.0220.0000.0000.0000.0290.0820.1071.0000.0270.0430.0000.0000.0320.0000.0000.0310.0000.000-0.0470.0000.0410.0550.0710.113-0.0570.0000.0670.0000.0000.0000.0000.0000.0470.0000.000-0.0640.0000.0000.0000.0000.000-0.0550.000-0.039-0.0830.000-0.0020.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0730.0000.0000.1430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
FK_STENOK0.1180.0000.0000.1180.0000.0610.0530.0000.0210.0271.0000.0630.0410.0670.0190.0330.0000.5400.0350.200-0.0190.0000.1170.077-0.0470.005-0.0270.0660.0000.0930.0700.0000.0000.0680.0000.0550.0420.0950.0390.0460.0000.0940.0000.8770.049-0.0160.0120.0000.0410.0000.0470.0580.1030.0000.0000.0550.0320.0680.0000.0000.0001.0000.0470.0150.0000.0000.1161.0000.0540.0240.1030.0000.0000.0680.0000.0000.0000.0700.0560.0270.0860.0000.0000.0970.0000.0000.0000.0000.0000.2030.0000.1140.0610.0000.0470.0000.0000.1260.0000.0690.0470.1180.0000.0400.0000.0000.0970.0000.0000.000
GB0.1050.0000.0920.0940.0400.0130.5560.1550.1640.0430.0631.0000.0000.0000.0830.0300.0000.0950.0620.0550.0270.0330.0000.062-0.0220.0000.0370.0360.0430.0000.0000.0370.0000.1400.0180.0940.0780.1110.0710.0000.0000.2380.2260.1190.0230.3290.3230.0310.0430.0000.0580.0530.0700.0500.0000.0000.0000.0600.0000.0000.0000.0000.0120.0750.0000.1290.0000.0000.0180.1380.0720.0000.0820.0030.0000.0000.0000.0000.0440.0540.0000.0000.0000.0350.0000.0000.0150.4040.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0000.0690.0000.0000.0430.0960.0000.000
GEPAR_S_n0.1260.0580.0720.1920.0260.0000.0000.1010.0680.0000.0410.0001.0000.0000.0840.0090.0000.0790.0140.000-0.0530.0300.0900.0710.0590.000-0.0820.1760.1100.0000.0480.0000.0000.0580.0280.1200.000-0.0700.0000.0440.0000.0470.033-0.0100.036-0.071-0.0610.000-0.1770.0000.0690.0590.0000.0000.0000.0390.0140.0800.0000.0140.0001.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.1020.0000.0070.0220.0000.0000.0000.0230.0000.0000.0000.000
GIPER_NA0.0590.0540.0220.0000.0270.0000.0000.0000.0000.0000.0670.0000.0001.0000.1000.0000.0000.0350.0000.0000.1190.0000.0000.0000.0010.0000.2690.0000.0000.0000.0000.0000.0210.0000.0000.0000.000-0.0810.0000.0000.0000.0000.000-0.0840.000-0.092-0.0430.000-0.0230.0340.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0740.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
GIPO_K0.0470.0000.0000.0000.0220.0000.0930.0000.0410.0320.0190.0830.0840.1001.0000.0000.1670.0520.0000.000-0.8360.0000.0000.108-0.0110.000-0.3210.0940.0630.0000.0000.0750.0370.0000.0000.0690.037-0.0270.0620.0000.0000.0210.0000.0250.0000.011-0.0290.000-0.0930.0230.0000.0000.0190.0070.0000.0000.0520.0590.0000.0000.0060.0000.0000.0000.0000.0000.0001.0000.0000.0230.0100.0000.0000.0000.0120.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0390.0000.0000.0000.0000.0000.0680.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
GT_POST0.0840.0000.0000.0000.0000.0000.0000.3810.0640.0000.0330.0300.0090.0000.0001.0001.0000.0210.0000.0370.0350.1720.0080.0530.0230.000-0.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0660.000-0.118-0.0810.000-0.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.2470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0440.0000.0000.0000.1250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
IBS_NASL0.0000.0000.0000.0000.1870.1980.0000.0000.0900.0000.0000.0000.0000.0000.1671.0001.0000.0001.0000.0000.1501.0000.0000.000-0.0650.0000.2670.0000.1710.0000.0500.0000.0810.0000.0600.0000.000-0.1170.2830.2320.0760.0000.0000.0011.0000.0890.4180.0000.0690.0000.0000.0000.0000.0001.0001.0001.0000.0001.0000.0000.0001.0000.0840.0001.0000.0001.0001.0001.0000.0001.0001.0000.0000.0000.0000.0001.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0001.0001.0000.0000.0000.0820.0000.0001.0001.0000.0000.0000.0000.0001.0000.0001.000
IBS_POST0.1430.0140.0460.0000.0180.0520.1340.0000.0000.0310.5400.0950.0790.0350.0520.0210.0001.0000.0410.2110.0560.0000.1160.092-0.1090.0440.0430.1530.0530.0690.0550.0000.0000.0210.0000.0930.0000.0870.0480.0030.0290.0840.0000.3140.0310.1230.0310.0340.0910.0710.0670.0880.0800.0000.0000.0500.0470.1120.0150.0000.0360.0000.0990.0550.0000.0100.0001.0000.0800.0540.0230.0540.0000.0000.0000.0000.0000.0830.0000.0000.0920.0000.0080.0000.0070.0420.0000.0000.0000.0000.0000.0000.0000.0200.0730.0230.0000.0640.0380.0000.0760.0000.0070.0220.0380.0230.0400.0000.0240.000
IM_PG_P0.0000.0000.0130.0000.0000.0000.0190.0510.0000.0000.0350.0620.0140.0000.0000.0001.0000.0411.0000.0000.0470.0000.0000.0000.0790.1250.0080.0500.1320.0350.0820.0900.0000.0000.0370.0000.000-0.0410.0000.0400.0790.0000.000-0.0120.000-0.072-0.0660.000-0.0780.0000.0000.0700.0000.0000.0000.0000.0130.0000.0000.0000.0230.0000.0930.0610.0000.0000.0000.0000.0950.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0170.0000.0000.0000.0620.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1160.0000.0990.0080.0000.0000.0000.0000.0000.000
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SEX0.3890.0950.0410.0000.1120.0370.3400.1730.0930.0000.0940.2380.0470.0000.0210.0000.0000.0840.0000.0230.0280.0220.0000.0910.0280.0410.0610.0370.0000.0000.0000.0540.0880.0460.0400.0000.0740.1940.0790.0000.0281.0000.058-0.0870.000-0.222-0.1520.020-0.0290.0840.1160.0380.2480.0900.0970.0220.0000.0780.0000.0000.0150.0000.0490.0430.0220.0000.0220.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0200.0700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0670.0340.0000.0000.0670.0150.0000.0000.0070.0000.000
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SVT_POST0.0000.0000.0370.0150.0000.0000.1490.0000.0000.0000.0490.0230.0360.0000.0000.0001.0000.0310.0000.0110.0000.0000.0000.0120.0000.0000.0000.0000.0000.0860.0490.0000.0000.0000.0500.0000.0660.0000.0200.0000.0160.0000.0000.0651.000-0.095-0.0290.0000.0010.0000.0790.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0980.0000.0000.1580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1630.0000.0000.0000.0850.0000.0000.0000.0000.0000.0370.0000.000
S_AD_KBRIG0.144-0.0070.113-0.002-0.0480.0230.3320.8380.441-0.039-0.0160.329-0.071-0.0920.011-0.1180.0890.123-0.0720.0170.013-0.084-0.091-0.060-0.130-0.003-0.057-0.056-0.010-0.0050.1090.0720.0570.0660.0910.1300.0450.0850.040-0.0040.053-0.2220.024-0.015-0.0951.0000.5110.0000.0970.0000.1390.1580.0640.0000.0000.0000.0720.1231.0001.0000.0001.0000.0530.0730.0000.0000.0001.0000.0000.1150.0000.0000.2670.0000.0340.0880.0500.0000.2710.0000.1620.0481.0001.0000.0001.0000.0001.0000.0001.0001.0000.0000.0000.0740.1360.0730.4770.1750.0000.1240.1110.0001.0000.1490.0000.0950.1080.0000.4770.000
S_AD_ORIT0.123-0.0560.1280.062-0.0580.1340.3880.3920.826-0.0830.0120.323-0.061-0.043-0.029-0.0810.4180.031-0.066-0.0430.050-0.0470.054-0.092-0.093-0.0140.076-0.0260.024-0.0310.0690.020-0.030-0.0120.048-0.049-0.0080.0500.002-0.0140.065-0.1520.1160.036-0.0290.5111.0000.0000.0910.0000.0000.0340.0780.0000.0000.0000.0170.0000.0001.0000.0000.0000.0410.0000.1010.1630.1011.0000.1210.0000.0000.0000.0720.0000.0480.0000.0000.0000.0000.0770.0710.0000.2710.0001.0000.0000.1740.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0831.0000.0270.0610.0590.0000.0000.0000.000
TIKL_S_n0.0630.0000.0000.1210.0000.0000.0640.0000.0340.0000.0000.0310.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0000.1540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0000.0200.0000.0090.0000.0000.0001.0000.0080.0650.0470.0000.0000.0000.0000.0000.0000.0790.0000.0000.0201.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.000
TIME_B_S0.0290.0040.0150.0290.0500.0880.0460.0530.086-0.0020.0410.043-0.177-0.023-0.093-0.0170.0690.091-0.078-0.0640.097-0.027-0.113-0.185-0.132-0.0150.090-0.287-0.239-0.057-0.032-0.028-0.104-0.037-0.033-0.1320.0120.142-0.102-0.020-0.062-0.0290.0450.0400.0010.0970.0910.0081.0000.0750.0480.0630.0660.0540.0800.0380.0580.1370.0000.0790.0120.0000.0690.0550.0000.0000.0840.0000.0730.0820.0260.0000.0000.0000.0400.0000.0210.0190.0910.0000.0000.0000.0800.0000.0000.1630.0000.0000.0000.0210.0210.0510.0790.0320.0000.0210.0700.0000.0570.0000.0000.0000.0090.0000.0000.0000.0310.0050.0000.000
TRENT_S_n0.0000.0000.0000.2430.0000.0180.0000.0000.0000.0000.0000.0000.0000.0340.0230.0000.0000.0710.0000.0000.1160.0000.0720.0000.0330.0000.0550.0940.0660.0000.0520.0340.0460.0230.0000.0000.0000.0000.0000.0000.0440.0840.0000.0610.0000.0000.0000.0650.0751.0000.0300.0000.0500.0000.0000.0000.0000.0800.0000.0570.0001.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0380.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.0000.0000.0320.0070.0000.0000.0000.032
ZSN_A0.0580.2160.0480.0270.0770.0000.0000.2170.0000.0000.0470.0580.0690.0000.0000.0000.0000.0670.0000.0860.0520.0000.0260.0570.0200.2300.0000.0000.0000.0610.0000.0160.0670.0000.0390.0560.1520.0000.0000.0000.0000.1160.0000.0380.0790.1390.0000.0470.0480.0301.0000.0860.1060.1500.0000.0000.0300.0330.0000.0000.0000.0000.0000.0430.0000.0710.0000.0000.0670.0000.1920.0000.0920.0000.1190.0000.0000.0000.0950.0700.2060.1380.0000.0000.0000.0000.1130.2420.0000.0000.2420.2810.1020.0380.2360.0000.0000.0780.0000.1450.1800.0000.0790.0940.0000.0000.0150.0000.0000.058
ant_im0.0000.0000.0620.0000.0670.1340.0600.2100.0330.0410.0580.0530.0590.0000.0000.0000.0000.0880.0700.0760.0410.0000.1270.2000.0650.0000.0430.2530.0950.0470.0630.0660.0390.0220.0510.0660.0000.0000.0180.0000.0370.0380.0000.0700.0000.1580.0340.0000.0630.0000.0861.0000.0540.0310.0390.0000.0770.0460.0600.0000.0000.0000.4070.3520.0000.0200.1440.0000.0560.1480.0550.0770.0000.0000.0000.0730.0450.0950.0840.0000.0000.0000.0000.0000.0000.0000.0560.0000.0210.0000.0000.1050.0000.0000.0001.0000.0000.0920.2290.0270.0000.0280.0000.1200.0500.0000.0000.0000.1130.069
endocr_010.1660.0000.0680.0000.0000.0680.1770.0000.0170.0000.1030.0700.0000.0000.0190.0000.0000.0800.0000.1090.0000.0000.0000.0570.1140.0000.0000.0000.0660.1000.0440.1200.0580.0190.0340.0000.0780.0700.0000.0860.0000.2480.0680.0770.0060.0640.0780.0000.0660.0500.1060.0541.0000.0720.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0680.0000.0000.0190.0790.0280.0270.0000.0000.0000.000
endocr_020.0170.0000.0000.0000.0000.0000.0830.0000.0540.0000.0000.0500.0000.0000.0070.0000.0000.0000.0000.0420.0000.0000.0000.0000.1990.0000.0000.0000.0410.0000.0000.0430.0000.0000.0000.0000.0000.0000.0340.0000.0000.0900.0000.0320.0000.0000.0000.0000.0540.0000.1500.0310.0721.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0850.0000.0000.1040.0520.0000.0000.0000.0000.0000.000
endocr_030.0650.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0290.0000.0000.0000.1220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0880.1120.0000.0000.0000.0970.0000.0220.0000.0000.0000.0000.0800.0000.0000.0390.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_010.0000.0000.0690.0000.0000.0770.0000.0000.0000.0000.0550.0000.0390.0000.0000.0001.0000.0500.0000.0450.0000.0000.0000.0180.1390.0000.1010.0000.0350.0000.0000.0000.0820.0210.0280.0000.0000.0000.0000.0000.0000.0220.0000.0100.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.000
fibr_ter_020.0400.0600.0000.0000.0000.0000.0000.0000.1210.0000.0320.0000.0140.0000.0520.0001.0000.0470.0130.0000.1210.0000.0070.0000.1040.0000.0000.0250.1190.0000.0490.0000.0000.0000.0480.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0720.0170.0000.0580.0000.0300.0770.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_030.1850.0000.0250.0570.0490.0000.0410.1720.0000.0000.0680.0600.0800.0420.0590.0000.0000.1120.0000.0660.0000.0000.0370.1040.0650.0080.0450.0470.0810.0000.0000.0000.0000.0000.0000.0000.0080.0510.0000.0000.0000.0780.0000.0800.0000.1230.0000.0790.1370.0800.0330.0460.0550.0000.0000.0000.0001.0000.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1500.0000.0000.0000.0000.0260.0590.0000.0000.0000.0000.000
fibr_ter_050.0000.1950.0000.0000.1110.0000.0201.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0150.0000.0000.0310.0001.0000.0550.1590.0000.0001.0000.2880.0000.0000.0000.0650.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0240.0001.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.1500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.000
fibr_ter_060.0970.0190.0000.0000.0360.0000.0631.0001.0000.0660.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0380.0000.0000.0480.0500.1650.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0790.0570.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_070.1670.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0060.0000.0000.0360.0230.0000.0000.0000.0000.0120.1150.0000.0000.0000.0000.0000.0600.0000.0000.0000.0980.0000.0000.0000.0000.0730.0300.0150.0000.0000.0000.0000.0000.0200.0120.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
fibr_ter_080.0001.0001.0001.0001.0001.0000.0001.0000.0000.0001.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0000.000
inf_im0.0000.0000.0000.0000.0000.1440.0000.0660.0620.0730.0470.0120.0480.0000.0000.0080.0840.0990.0930.0000.0000.0000.0620.1100.0370.0480.0510.1220.0710.0580.0000.0650.0000.0530.0150.0000.0000.0230.0000.0310.0310.0490.0000.0560.0980.0530.0410.0790.0690.0000.0000.4070.0000.0000.0140.0000.0310.0680.0000.0000.0000.0001.0000.2800.0000.0830.0000.0880.0940.1070.0000.0130.0520.0000.0000.1670.0260.0190.0000.0340.0260.0560.0470.1390.0460.0000.0000.0000.0750.0000.0000.0000.0000.0000.0001.0000.0000.0000.2040.0000.0740.0940.0740.0960.0460.0870.0520.0000.0000.023
lat_im0.0600.0000.0640.0000.0270.1320.0820.1790.0470.0000.0150.0750.0000.0740.0000.0000.0000.0550.0610.0510.0590.0680.0630.1250.0700.0380.0720.0850.0700.0000.0590.0000.0260.0560.0620.0510.0300.0000.0620.0000.0820.0430.0000.0310.0000.0730.0000.0000.0550.0600.0430.3520.0000.0000.0000.0350.0000.0000.1500.0670.0000.0000.2801.0000.0000.0000.0000.0000.0680.1360.0000.0000.0210.0000.0670.0930.0740.0430.0290.0190.0000.0000.0000.0000.0000.0000.0470.0000.0340.0000.0000.0000.0630.0000.0001.0000.0770.0000.1300.0080.0040.0000.0000.1170.0880.0000.0360.0250.0460.000
n_p_ecg_p_010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0000.0000.0520.0000.1240.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.1010.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0510.0000.0000.0000.0000.0000.000
n_p_ecg_p_030.0240.0420.0590.0000.0000.0000.0000.1550.1600.1430.0000.1290.0000.0000.0000.0000.0000.0100.0000.0630.0000.0000.0000.0000.0000.0000.0710.0130.0000.0170.0000.0000.0000.0000.0000.0000.0000.0460.0000.0350.0000.0000.0000.0820.1580.0000.1630.0000.0000.0000.0710.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0830.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.000
n_p_ecg_p_040.0000.0000.0000.0000.0000.0000.0680.0000.0000.0000.1160.0000.0000.0000.0000.0001.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0450.0000.0000.0000.1010.0000.0840.0000.0000.1440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2470.000
n_p_ecg_p_050.0361.0000.0000.0001.0000.0000.0001.0001.0000.0001.0000.0000.0001.0001.0000.0001.0001.0000.0000.0001.0000.0001.0000.0000.0670.0001.0001.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0880.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.000
n_p_ecg_p_060.0110.0000.0000.0000.1160.0000.0000.0000.1230.0000.0540.0180.0000.0000.0000.0001.0000.0800.0950.0360.0000.0000.0000.0000.3590.0000.0000.0000.0000.0000.0000.0310.0810.0400.0380.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.1210.0000.0730.0000.0670.0560.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0680.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0720.0000.3280.1720.0000.0000.0000.0460.0000.0000.000
n_p_ecg_p_070.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0240.1380.0000.0000.0230.0000.0000.0540.0000.0940.0000.0000.0000.0630.1100.0000.0380.0280.0000.0000.0000.0310.0460.0330.0000.0000.0000.0000.0370.0000.0000.0000.0000.0580.0000.1150.0000.0000.0820.0000.0000.1480.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.1070.1360.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0240.0000.0000.0330.0350.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0240.0000.0460.0150.0000.0000.0370.0000.0000.0000.0770.0120.0000.0240.0000.0150.000
n_p_ecg_p_080.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.1030.0720.0000.0000.0100.0001.0000.0230.0000.0000.1810.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0260.0000.1920.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1720.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0570.0000.0000.0000.0000.0000.042
n_p_ecg_p_090.1030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0540.0000.0570.0000.0000.0000.0000.0830.0000.0000.0000.0560.0000.0000.0310.0000.0000.0000.0000.0520.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
n_p_ecg_p_100.0810.0000.0000.0000.1140.0290.0390.3810.0270.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0540.0490.0000.0000.0000.0000.0000.0740.0000.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.2670.0720.0000.0000.0000.0920.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0210.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0290.0510.0000.0000.0000.0000.0000.0000.0000.1040.1110.3360.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.1070.0000.0190.0000.0490.0270.0000.0000.0000.0000.000
n_p_ecg_p_110.0000.0300.0120.0280.0000.0000.0000.0000.0000.0000.0680.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0000.0250.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0200.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0370.0000.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0730.0000.0000.0000.1900.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.000
n_p_ecg_p_120.1150.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0500.0420.0000.0000.0000.0210.0000.0000.0000.0000.1410.0000.0000.0000.0000.0000.0160.0000.0000.0000.1050.0000.0000.0000.0000.0000.0000.0340.0480.0000.0400.0380.1190.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.0000.0000.0670.0000.0000.0000.0000.0000.0240.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0400.0000.0000.0000.1620.0000.0000.0000.0000.0000.0000.0390.0000.0520.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.000
n_r_ecg_p_010.1470.0000.0000.0000.0000.0340.0000.0990.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1550.0000.0120.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0880.0000.0000.0000.0000.0000.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1670.0930.0380.0210.0000.0670.0000.0000.0000.0610.0000.0000.0001.0000.0000.0650.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0370.0000.0000.0000.0000.0370.0000.0860.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.000
n_r_ecg_p_020.0000.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.0210.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0740.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
n_r_ecg_p_030.0000.0000.0000.0000.0180.0300.0680.0000.0200.0000.0700.0000.0000.0000.0000.0000.0000.0830.0000.0000.0000.0000.0000.2120.0900.0420.0000.0530.0800.0210.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0000.0000.0190.0510.0000.0950.0000.0000.0000.1100.0510.0900.0000.0100.0100.0000.0190.0430.0000.0000.0000.0000.0310.0330.0000.0000.0290.0370.0000.0650.0001.0000.0740.0510.0230.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0220.0000.0090.0000.0000.0000.0000.0530.0000.0110.0290.0000.0860.0380.0000.0000.000
n_r_ecg_p_040.0000.1480.0000.0000.0600.0500.0570.2330.0000.0000.0560.0440.0580.0000.0250.0000.0000.0000.0000.0190.0000.0000.0610.1570.0000.0000.0240.0750.0540.0000.0000.0000.0000.0000.0000.0000.0000.0490.0550.0000.0000.0200.0000.0770.0000.2710.0000.0310.0910.0000.0950.0840.0040.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0350.0000.0000.0510.0000.0000.0040.0000.0741.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0800.0000.0000.000
n_r_ecg_p_050.0690.1310.0000.0000.0710.0000.0000.0000.0800.0000.0270.0540.0000.0000.0000.0000.0000.0000.0170.0050.0000.0000.0170.0000.0680.4470.0000.0000.0400.0180.0000.0000.0000.0000.0330.0000.0100.0000.0190.0000.0000.0700.0000.0000.0000.0000.0770.0000.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0190.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0510.0001.0000.0000.0000.0000.0000.0000.0000.0720.0000.0000.0000.0000.0000.0000.2980.0000.0000.0000.0820.0530.1810.5590.0000.0000.0370.0000.0000.0000.0000.0000.000
n_r_ecg_p_060.0780.0550.0000.0440.0000.0000.0000.2150.0270.0000.0860.0000.0000.0000.0000.0471.0000.0920.0000.0000.0210.0780.0000.0000.0000.4810.0290.0400.0000.0170.0000.0000.0000.0000.0000.0300.0000.0420.0000.0000.0000.0000.0000.1080.0000.1620.0710.0000.0000.0000.2060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0230.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.8550.0000.0000.0000.0000.1860.5360.0220.0000.0420.0000.0000.0000.0000.0000.000
n_r_ecg_p_080.0660.0000.0000.0180.0000.0000.1520.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1660.0000.0000.0560.0000.0000.0000.0000.0000.0380.1120.0000.0000.0000.0000.0000.0000.0260.6440.0480.0000.0000.0000.0000.1380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1440.0000.0330.0000.1270.0000.0000.0000.0000.0000.0650.0000.000
n_r_ecg_p_090.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.2471.0000.0080.0000.0220.0001.0000.0000.0000.0670.0000.0001.0000.1040.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.2710.0000.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
n_r_ecg_p_100.0000.0880.0000.0000.1980.0000.0001.0000.0000.0000.0970.0350.0000.0000.0000.0001.0000.0000.0620.0000.0000.0000.0000.0320.0000.0320.1030.0000.0660.0000.0000.0110.0000.0670.0000.0000.0000.0000.0000.0550.0000.0000.0000.0610.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1110.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.000
np_010.0000.0000.0000.0000.0000.0000.1110.0001.0000.0000.0000.0000.0000.0000.0000.0001.0000.0070.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.2530.0000.0000.0000.0000.0000.0000.0000.0000.0000.1940.0000.0000.0000.0480.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_040.0000.0000.0300.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0420.0000.0570.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0001.0000.0000.0000.1630.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_050.1090.0000.0000.0000.0000.0000.0000.2320.1880.0440.0000.0150.0000.0000.0000.0001.0000.0000.0000.0720.0090.0000.0000.0180.0000.0870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.1740.0770.0000.0030.1130.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0760.0000.0000.1040.0000.0000.0000.0000.0000.0000.0720.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0100.0000.0000.1540.0000.0000.0000.0490.0980.0000.0000.0000.0000.0000.0000.0000.0000.000
np_070.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.4040.0000.0000.0000.0001.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.2420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1110.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.000
np_080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0340.0000.0000.0000.0000.0000.0000.0000.0000.3360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_090.0890.0000.0000.0000.0000.0000.0001.0001.0000.0000.2030.0000.0000.0000.0000.0001.0000.0000.0610.1310.1110.0000.0000.0000.0200.0000.0000.0000.1640.0770.0000.0000.0000.0000.0000.0000.0000.0000.0950.1310.0000.0000.0000.0000.0001.0001.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1620.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
np_100.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0001.0001.0000.0001.0000.0000.0000.0001.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0210.0000.2420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.000
nr_010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1140.0280.0000.0000.0000.0001.0000.0000.0000.0550.2710.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.1390.0000.0000.0550.0000.0210.0000.0170.0000.0000.0000.0000.0510.0000.2810.1050.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1720.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
nr_020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0610.0000.0380.0000.0390.0420.0000.0000.0000.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0100.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.1020.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0630.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
nr_030.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0450.0000.0000.0000.0000.0000.2030.0000.0000.0000.0000.0000.0460.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0000.0340.0000.0740.0000.0000.0320.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0220.0000.2980.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0390.0720.1810.0000.0000.0000.0000.0000.0000.0000.0000.000
nr_040.0560.0000.0000.0330.0000.0000.0000.2120.0000.0000.0470.0000.0000.0000.0000.0441.0000.0730.0000.0120.0490.0860.0000.0000.0240.4520.0460.0440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0950.0000.1360.0000.0000.0000.0000.2360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0730.0000.0000.0000.0000.0000.0000.8550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.1570.4970.0000.0000.0290.0000.0000.0000.0000.0000.000
nr_070.0000.0840.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0230.0000.1280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0730.0000.0000.0210.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.1540.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
nr_080.0000.0000.0000.0000.0000.0000.0000.3800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.3400.0000.0000.0000.3420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.4770.0000.0000.0700.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0390.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.2470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2000.000
nr_110.0990.0000.0000.0000.0410.0000.0510.2670.0620.0000.1260.0000.0000.0000.0000.0000.0000.0640.0000.0420.0000.0000.0090.0000.0000.0940.0000.0000.0230.0470.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1110.1630.1750.0000.0000.0000.0000.0780.0920.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.0000.0000.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0820.0000.1440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0600.0150.0000.0000.0000.0350.0000.0000.0420.0000.000
post_im0.0000.0000.0000.0260.0470.1320.0000.0000.0000.0200.0000.0000.1020.0000.0680.1250.0820.0380.0000.0350.0880.0000.0000.0700.0000.0000.0620.1920.0710.0960.0000.0160.0000.0790.0150.0900.0220.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0470.0570.0830.0000.2290.0000.0000.0000.0000.0000.1500.0680.0000.0000.0000.2040.1300.0000.0000.0000.0000.0000.0000.0000.0000.0000.1900.0520.0860.0000.0000.0000.0530.0000.0000.0000.1110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0800.0000.0000.0000.0000.000
ritm_ecg_p_010.0910.0000.0080.0000.0670.0290.1300.0380.0720.0000.0690.0750.0000.0000.0000.0000.0000.0000.0000.0590.0000.0330.0000.0000.0960.2920.0970.0000.0000.0000.0000.0850.0290.0000.0000.0000.0890.0940.0000.0000.0000.0670.0330.0660.0000.1240.0000.0000.0000.0000.1450.0270.0680.0850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0330.0000.0000.0000.0720.0370.0350.0000.1070.0000.0000.0000.0000.0000.0000.1810.1860.0330.0000.0000.0000.0000.0491.0000.0000.0000.0000.0000.0000.0720.1570.0000.0000.0600.0001.0000.3230.1120.0000.8000.2810.0000.0720.0000.0000.065
ritm_ecg_p_020.0730.0820.0260.0150.0000.0000.0000.1000.0680.0000.0470.0000.0070.0000.0000.0000.0000.0760.1160.0000.0000.0340.0460.0000.0000.8260.1090.0000.0360.0210.0000.0110.0000.0000.0000.0080.0000.0000.0000.0000.0000.0340.0000.0860.0000.1110.0000.0000.0000.0000.1800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0740.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0080.5590.5360.0000.0000.0640.0000.0000.0981.0000.0000.0000.0000.0000.0000.1810.4970.0000.0000.0150.0000.3231.0000.0000.0000.0930.0000.0000.0000.0230.0000.000
ritm_ecg_p_040.0000.0000.0000.0000.0000.0000.0000.1590.1230.0000.1180.0000.0220.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0640.0000.0000.0000.0270.0850.0000.0830.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0000.0000.0000.3280.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.0220.1270.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1120.0001.0000.0000.0000.0000.0000.0000.0280.0000.000
ritm_ecg_p_060.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0001.0001.0000.0001.0000.0070.0990.0001.0000.0001.0000.0000.9960.0001.0001.0000.0000.0000.0000.0440.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0090.0000.0790.0000.0190.1040.0000.0000.0000.0000.0000.0000.0000.0000.0740.0000.0000.0000.0000.0000.1720.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
ritm_ecg_p_070.0430.0340.0000.0260.0460.0000.0920.0920.0840.0000.0400.0690.0000.0000.0000.0000.0000.0220.0080.0660.0000.0000.0000.0090.0700.0450.0420.0000.0250.0000.0000.0820.0660.0340.0000.0000.0990.1230.0000.0000.0000.0670.0000.0000.0000.1490.0270.0150.0000.0000.0940.1200.0790.0520.0000.0000.0000.0260.0000.0000.0000.0000.0960.1170.0510.0310.0000.0000.0000.0770.0570.0000.0490.0000.0000.0000.0000.0290.0000.0370.0420.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.8000.0930.0000.0001.0000.0780.0000.0630.0000.0000.042
ritm_ecg_p_080.0000.0000.0230.0000.0000.0170.0140.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0380.0000.0120.0500.0000.0350.0000.0000.0000.0000.0000.0000.0170.0000.0210.0000.0000.0170.0000.0000.0000.0000.0360.0000.0150.0000.0000.0000.0000.0610.0000.0000.0320.0000.0500.0280.0000.0000.0000.0000.0590.0270.0000.0000.0000.0460.0880.0000.0000.0000.0000.0000.0120.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0350.0800.2810.0000.0000.0000.0781.0000.0000.0000.0000.0000.000
zab_leg_010.0720.0530.0220.0140.0000.0330.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0230.0000.0020.0000.0000.0470.0000.0000.0000.0000.0000.0250.0000.0410.0000.0000.0700.0540.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0950.0590.0000.0000.0070.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0860.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0640.0000.0000.014
zab_leg_020.0460.1890.0220.0180.0000.0280.0790.1890.0560.0000.0970.0430.0000.0000.0000.0000.0000.0400.0000.0000.0860.0000.0000.0870.0000.0000.0820.0960.0980.0430.0000.0870.0000.0440.0000.0000.0490.0000.0190.0290.0210.0000.0000.0900.0000.1080.0000.0000.0310.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0520.0360.0000.0000.0000.0550.0460.0240.0000.0000.0000.0000.0210.0000.0000.0380.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0720.0000.0000.0000.0630.0000.0641.0000.0000.0000.000
zab_leg_030.0320.0000.0000.0000.0000.0000.0000.0000.2780.0000.0000.0960.0000.0000.0000.0001.0000.0000.0000.0050.0000.0000.0000.0000.1240.0150.0340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0370.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0230.0280.0000.0000.0000.0000.0001.0000.0000.000
zab_leg_040.0140.0000.0000.0160.0000.0000.0000.3800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.2820.0000.0000.0000.2760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.4770.0000.0000.0000.0000.0000.1130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0000.0000.2470.0000.0000.0150.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
zab_leg_060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.0370.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0580.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0000.0420.0000.0140.0000.0000.0001.000

Missing values

2024-05-17T16:16:00.194447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-17T16:16:01.174344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AGESEXINF_ANAMSTENOK_ANFK_STENOKIBS_POSTIBS_NASLGBSIM_GIPERTDLIT_AGZSN_Anr_11nr_01nr_02nr_03nr_04nr_07nr_08np_01np_04np_05np_07np_08np_09np_10endocr_01endocr_02endocr_03zab_leg_01zab_leg_02zab_leg_03zab_leg_04zab_leg_06S_AD_KBRIGD_AD_KBRIGS_AD_ORITD_AD_ORITO_L_POSTK_SH_POSTMP_TP_POSTSVT_POSTGT_POSTFIB_G_POSTant_imlat_iminf_impost_imIM_PG_Pritm_ecg_p_01ritm_ecg_p_02ritm_ecg_p_04ritm_ecg_p_06ritm_ecg_p_07ritm_ecg_p_08n_r_ecg_p_01n_r_ecg_p_02n_r_ecg_p_03n_r_ecg_p_04n_r_ecg_p_05n_r_ecg_p_06n_r_ecg_p_08n_r_ecg_p_09n_r_ecg_p_10n_p_ecg_p_01n_p_ecg_p_03n_p_ecg_p_04n_p_ecg_p_05n_p_ecg_p_06n_p_ecg_p_07n_p_ecg_p_08n_p_ecg_p_09n_p_ecg_p_10n_p_ecg_p_11n_p_ecg_p_12fibr_ter_01fibr_ter_02fibr_ter_03fibr_ter_05fibr_ter_06fibr_ter_07fibr_ter_08GIPO_KK_BLOODGIPER_NANA_BLOODALT_BLOODAST_BLOODKFK_BLOODL_BLOODROETIME_B_SR_AB_1_nR_AB_2_nR_AB_3_nNA_KBNOT_NA_KBLID_KBNITR_SNA_R_1_nNA_R_2_nNA_R_3_nNOT_NA_1_nNOT_NA_2_nNOT_NA_3_nLID_S_nB_BLOK_S_nANT_CA_S_nGEPAR_S_nASP_S_nTIKL_S_nTRENT_S_n
077.012.01.01.02.0NaN3.00.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN180.0100.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.04.70.0138.0NaNNaNNaN8.016.04.00.00.01.0NaNNaNNaN0.00.00.00.00.00.00.01.00.00.01.01.00.00.0
155.011.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN120.090.00.00.00.00.00.00.04.01.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.50.0132.00.380.18NaN7.83.02.00.00.00.01.00.01.00.00.00.00.01.00.00.01.00.01.01.01.00.01.0
252.010.00.00.02.0NaN2.00.02.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0150.0100.0180.0100.00.00.00.00.00.00.04.01.00.00.00.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.04.00.0132.00.300.11NaN10.8NaN3.03.00.00.01.01.01.00.01.00.00.03.02.02.01.01.00.01.01.00.00.0
368.000.00.00.02.0NaN2.00.03.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.0NaNNaN120.070.00.00.00.00.00.00.00.01.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.90.0146.00.750.37NaNNaNNaN2.00.00.01.0NaNNaNNaN0.00.00.00.00.00.00.00.00.01.01.01.00.00.0
460.010.00.00.02.0NaN3.00.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0190.0100.0160.090.00.00.00.00.00.00.04.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.50.0132.00.450.22NaN8.3NaN9.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.01.0
564.010.01.02.01.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN140.090.00.00.00.00.00.00.01.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaNNaNNaN0.450.22NaN7.22.02.00.00.00.00.01.00.00.00.00.00.00.00.00.00.01.00.01.01.00.00.0
670.011.01.02.01.0NaN2.00.07.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.0120.080.0120.080.00.00.00.00.00.00.00.00.03.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaNNaNNaN0.300.11NaN11.15.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.0
765.010.01.01.02.0NaN2.00.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN145.095.00.00.00.00.00.00.00.00.02.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.04.50.0136.0NaNNaNNaN6.220.07.03.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.01.00.00.0
860.010.00.00.02.0NaN2.00.06.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0200.0120.0195.0120.00.00.00.00.00.00.00.00.03.02.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaNNaNNaN0.300.37NaN6.23.03.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.0NaNNaNNaNNaN
977.002.00.00.00.0NaN3.00.06.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.0NaNNaN200.0100.00.00.00.00.00.00.04.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaNNaNNaN0.380.11NaN6.930.03.00.00.00.0NaNNaNNaN0.00.00.00.01.00.00.00.00.01.00.00.00.00.0
AGESEXINF_ANAMSTENOK_ANFK_STENOKIBS_POSTIBS_NASLGBSIM_GIPERTDLIT_AGZSN_Anr_11nr_01nr_02nr_03nr_04nr_07nr_08np_01np_04np_05np_07np_08np_09np_10endocr_01endocr_02endocr_03zab_leg_01zab_leg_02zab_leg_03zab_leg_04zab_leg_06S_AD_KBRIGD_AD_KBRIGS_AD_ORITD_AD_ORITO_L_POSTK_SH_POSTMP_TP_POSTSVT_POSTGT_POSTFIB_G_POSTant_imlat_iminf_impost_imIM_PG_Pritm_ecg_p_01ritm_ecg_p_02ritm_ecg_p_04ritm_ecg_p_06ritm_ecg_p_07ritm_ecg_p_08n_r_ecg_p_01n_r_ecg_p_02n_r_ecg_p_03n_r_ecg_p_04n_r_ecg_p_05n_r_ecg_p_06n_r_ecg_p_08n_r_ecg_p_09n_r_ecg_p_10n_p_ecg_p_01n_p_ecg_p_03n_p_ecg_p_04n_p_ecg_p_05n_p_ecg_p_06n_p_ecg_p_07n_p_ecg_p_08n_p_ecg_p_09n_p_ecg_p_10n_p_ecg_p_11n_p_ecg_p_12fibr_ter_01fibr_ter_02fibr_ter_03fibr_ter_05fibr_ter_06fibr_ter_07fibr_ter_08GIPO_KK_BLOODGIPER_NANA_BLOODALT_BLOODAST_BLOODKFK_BLOODL_BLOODROETIME_B_SR_AB_1_nR_AB_2_nR_AB_3_nNA_KBNOT_NA_KBLID_KBNITR_SNA_R_1_nNA_R_2_nNA_R_3_nNOT_NA_1_nNOT_NA_2_nNOT_NA_3_nLID_S_nB_BLOK_S_nANT_CA_S_nGEPAR_S_nASP_S_nTIKL_S_nTRENT_S_n
118062.000.02.02.01.0NaN2.00.0NaN0.01.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.0115.070.0140.090.00.00.01.00.00.00.0NaNNaNNaNNaN0.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.04.00.0130.00.230.11NaN5.010.04.00.01.00.01.00.01.00.00.01.00.00.00.00.01.00.01.01.01.00.00.0
118181.000.0NaN3.01.0NaN0.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0180.0110.0160.090.00.00.01.00.00.00.00.00.04.00.00.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.30.0134.00.380.30NaN10.64.07.00.00.00.01.01.01.00.01.00.00.00.00.00.01.00.01.00.01.00.00.0
118276.000.00.00.02.0NaN2.00.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaNNaNNaN0.00.00.00.00.00.00.00.04.00.00.0NaNNaNNaNNaNNaNNaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.04.10.0140.00.230.33NaN9.622.08.01.00.00.01.01.00.00.01.00.00.00.00.00.01.00.01.00.01.00.00.0
118367.011.02.02.01.0NaN3.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN120.080.00.00.00.00.00.00.00.01.04.02.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.40.0135.00.150.07NaN6.515.02.01.01.00.0NaNNaNNaN0.01.01.01.00.00.00.01.00.00.01.01.00.00.0
118454.010.00.00.00.0NaN2.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN160.0100.00.00.00.00.00.00.00.01.02.01.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.60.0135.00.380.22NaN9.0NaN8.00.00.00.0NaNNaNNaN0.01.00.00.00.00.00.01.00.01.00.01.00.00.0
118578.010.00.00.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN130.080.00.00.00.00.00.00.04.02.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.04.20.0141.00.300.15NaN8.233.03.00.00.00.0NaNNaNNaN0.00.00.00.00.00.00.00.00.01.00.01.00.00.0
118659.011.03.02.01.0NaN2.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.0130.090.0170.0110.00.00.00.00.00.00.04.03.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.00.00.00.00.00.00.04.50.0135.00.380.22NaN8.8NaN2.00.00.00.01.01.00.00.02.01.00.00.00.00.01.01.00.01.01.00.00.0
118769.000.06.02.01.0NaN2.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0210.0110.0110.070.00.00.00.00.00.00.01.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaNNaNNaNNaNNaNNaN11.816.07.00.00.00.01.01.01.00.00.00.00.00.00.00.00.00.01.00.01.00.00.0
118853.010.00.00.00.0NaN2.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN90.060.00.00.00.00.00.00.04.01.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.04.00.0139.00.450.33NaN18.66.09.00.00.00.0NaNNaNNaN0.01.00.00.00.00.00.01.00.01.00.01.00.00.0
118959.011.03.02.02.0NaN2.00.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaN160.090.00.00.00.00.00.00.04.04.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNaNNaNNaNNaNNaNNaN9.725.0NaN1.02.01.0NaNNaNNaN0.01.02.01.00.00.00.01.00.00.00.01.00.00.0